latin american conference 2.0 on complex networks · 2019-08-06 · graph database for analysis and...

agosto 05-09

2.0 ON COMPLEX NETWORKs LATIN AMeRICAN CONFERENCE

CARTAGENA - colombia

sponsored by:

1Mini symposium

2Ignite Talks

3Pararells

C O N T E N I D O

BIOLOGICAL NETWORKSConstruction of Gene Regulatory Network using RNA Sequencing data based on graphical models

BIOLOGICAL NETWORKSLong-Range Interactions in Geometric Neuronal Networks

BIOLOGICAL NETWORKSEnrichment of modular structures in biological networks: A guideline for comparison

CITIES & NETWORKS: CHALLENGES IN SUSTAINABLE URBAN METABOLISMMidiendo la complejidad de los proyectos de Infraestructura de transporte vial: análisis a través de una red compleja.CITIES & NETWORKS: CHALLENGES IN SUSTAINABLE URBAN METABOLISMMidiendo la complejidad de los proyectos de Infraestructura de transporte vial: análisis a través de una red compleja.CITIES & NETWORKS: CHALLENGES IN SUSTAINABLE URBAN METABOLISMModelado de la gestión del agua en la región hídrica Bogotá – Colombia

CITIES & NETWORKS: CHALLENGES IN SUSTAINABLE URBAN METABOLISMTráfico urbano de cannabis modelado por una red compleja

CITIES & NETWORKS: CHALLENGES IN SUSTAINABLE URBAN METABOLISMAssessing resilience of the Colombian Power Grid through percolation analysis

CITIES & NETWORKS: CHALLENGES IN SUSTAINABLE URBAN METABOLISMFrom centrality measures to persistent homology in metropolitan areas

CITIES & NETWORKS: CHALLENGES IN SUSTAINABLE URBAN METABOLISMIdentification of Thefts Patterns with Space-Time Event Networks

COMPLEX NETWORKS IN ECOLOGY: FROM BACTERIA TO MAMMALSEl rol de los parasitoides en el sistema Silene-Hadena: un enfoque estructural y dinámico

NETWORK PROPERTIES OF TOPOLOGICAL DATA ANALYSISThe Topology of Communicating Across Cities of Increasing Sizes, or the Complex Task of ”Reaching Out” in Larger Cities

BIOLOGICAL NETWORKSA topological model of a neuron-glia network: hypergraph and natural structures approachBIOLOGICAL NETWORKSFunctional connectivity in 3D cultured neuronal networks.BIOLOGICAL NETWORKSFast Correlation-Based Filter algorithm andbuilding of supervised co-expression networksBIOLOGICAL NETWORKSAssessment of Gene Regulatory Network Inference Algorithms Using Monte Carlo SimulationBIOLOGICAL NETWORKSMinimal gene regulatory network robustness analysisCLIMATE & ECOSCIENCE IN THE NATIONAL PARKS: DYNAMICS ASSOCIATED WITH US POLICYCLIMATE & ECOEVALUATION OF CLIMATIC MULTIDIMENSIONAL COORDINATION IN THE WESTERN US

CLIMATE & ECO19S – MEXICO’S RESILIENCE IN AN EARTHQUAKE EVENCLIMATE & ECONetwork analysis of El NiñoCLIMATE & ECOComunidades de briofitos en bosques de Polylepis sericea Wedd. de la Sierra Nevada de Mérida (Venezuela): una aproximación desde la perspectiva de redes complejas

GLEE: Geometric Laplacian Eigenmap Embedding

Characterizing complex networks using Information Theory quantifiers

ET goes home: Quantifying the returns to return mobility

Functional Connectivity in Engineered Neuronal Cultures: Challenges and Opportunities

What Is the Human Place in Ecological Networks?

The complexity of political corruption networks

COMPLEX NETWORKS IN ECOLOGY: FROM BACTERIA TO MAMMALSA quick review of ecology of bacteria

COMPUTATIONAL MASS MIGRATIONS: BIG DATA TO UNDERSTAND MOBILITYTemporal Analysis of the Asylum Seekers Network

NETWORK PROPERTIES OF TOPOLOGICAL DATA ANALYSISGraph Database for Analysis and Modelling of Microgrids based on Complex Networks Theory

NETWORK PROPERTIES OF TOPOLOGICAL DATA ANALYSISMulti-scale Anomaly Detection on Attributed Networks

NETWORK PROPERTIES OF TOPOLOGICAL DATA ANALYSISNetwork Embeddings For Graph Classification, Clustering and Visualization

CITIES & NETWORKS: CHALLENGES IN SUSTAINABLE URBAN METABOLISMEstudio teórico de la dinámica de propagación del VIH en una población que interactúa en una red compleja de contactos sexuales

NETWORK PROPERTIES OF TOPOLOGICAL DATA ANALYSISThe influence of EEG references on the analysis of spatio-temporal interrelation patterns

NETWORK MEDICINE & HEALTHWhole-Transcriptome Regulatory Networks of Lung Cancer

DYNAMICSRelation of basin stability of perturbed power systems on network heterogeneity and tripping timeDYNAMICSThe effect of interaction profiles on the complexity in a competitive-cooperative-mixed multiagent system

DYNAMICSExtracting multifractal properties from visibility networks

DYNAMICSThe Stochastic Transport Dynamics of a Conserved Quantity on a Complex Network

DYNAMICSAn Ising model of opinion formation in dialectic processes with opposing arguments

DYNAMICSJoint characterization of phase synchronization in networks with multivariate singular spectrum analysis and vector field phaseDYNAMICSPowerNet - A Toolbox for Network-based Analysis of Power Systems

DYNAMICSDynamical stability analysis of the Colombian Power Grid

DYNAMICSDynamical Control of the Diffusion Process on Social Networks

ECONOMIC & KNOWLEDGE The Knowledge Transfer from Universities to Regions as an Epidemic Process

ECONOMIC & KNOWLEDGE Opinion mining in the Colombian banking sector through community detection and topic modeling in the social network Twitter

ECONOMIC & KNOWLEDGE Structure and evolution of collaborative relationships in supply chain global projects

ECONOMIC & KNOWLEDGE The network dynamics of urban agglomeration

ECONOMIC & KNOWLEDGE Multi-Layer Weighted Network for Aguascalientes SCIAN 4-digits Economic Sectors

INTERDISCIPLINARYModelling fear of crime in cities

INTERDISCIPLINARYNetworks of melody: The complex use of tonal consonance in music

INTERDISCIPLINARYEstructura silábica de los 'Cien sonetos de amor' de Pablo Neruda

INTERDISCIPLINARYCentralidad y poder en los mundiales de Fútbol: un ejercicio didácticoINTERDISCIPLINARYDesigning an Information System for Measuring and Tracking Research: a Complex Systems ApproachMODELSNetwork Formation and Equilibrium OutcomesMODELSA class of probabilistic multilayer networksMODELSSelf-organization via consecutive preferential attachment episodesNETWORK MEDICINE & HEALTHLoss of heterogeneity in lung cancer networks

NETWORK MEDICINE & HEALTHFrom healthy aging to Alzheimer’s disease: A multilayer network approachNETWORK MEDICINE & HEALTHAnálisis de la configuración de los determinantes de la salud desde las Redes Bipartitas

NETWORK MEDICINE & HEALTHCaracterización del estado de salud de poblaciones a partir de propiedades estadísticas de patrones de morbilidadNETWORK MEDICINE & HEALTHUnique deregulation patterns in breast cancer metabolic networks

NETWORK MEDICINE & HEALTHLeveraging network and cross-impact balance (CIB) analyses to understand the influence of transportation and food systems on health in Latin AmericaNETWORK MEDICINE & HEALTHDifferential identifiability improves connectome predictive modeling of cognitive deficits in Alzheimer’s DiseaseNETWORK MEDICINE & HEALTHTeoria de Redes Complexas como abordagem para análise da acessibilidade geográfica da oferta potencial de serviços de saúde brasileirosROBUSTNESSAnalysis of the relationship between multifractality and robustness in complex networks

ROBUSTNESSSystemic Performance Measures from Distributional Zeta-Function

ROBUSTNESSStructural stability of interaction networks against negative external fieldsSOCIAL NETWORKSAnalysis of Weighted Networks of Friendship and Animosity in Classrooms Groups

SOCIAL NETWORKSEstudio de difusión de información en temáticas de violencia en redes sociales

SOCIAL NETWORKSCross-class networks in college: A Temporal social network analysis

INTERDISCIPLINARYModelling fear of crime in cities

INTERDISCIPLINARYNetworks of melody: The complex use of tonal consonance in music

SOCIAL NETWORKSTemporal Social Network Analysis Of A Physical Activity Mobile Phone-based Intervention in Public Schools Of Bogotá, Colombia.STRUCTURERandom Matrix Theory approach to random bipartite graphsSTRUCTUREVRP Complex Network Analysis and SimulationSTRUCTUREMessage Passing Approximation of the Boltzmannian Minimum Description Length for Community Detection in Complex NetworksSTRUCTUREModular Decomposition Applications to Network Analysis

Posters

4Taste does not endure: The dynamics of ingredient pairings, Colombia, 1977-2017

‘Los mismos con las mismas’? Closeness and political-elite persistence within the Colombian presidential kin network between 1811 and 2018.

Empirical analysis of the European international railway network

Modelado de la red compleja de actividades a través de la dinámica de sistemas para un proyecto de infraestructura de transporte vial.

Algorithmic Complexity As a Better Measure In a Real Social Network

On functional observability, sensor allocation and dynamical networks

About the interaction between active substances and diseasesAbrupt percolation transition on random networks induced by attacks on high-betweenness nodesRED DE INTERACCIONES PLANTA-POLINIZADOR EN UN BOSQUE INUNDABLE DE LA CUENCA DEL LAGO DE MARACAIBO (VENEZUELA)Commodore micro-RNAs in breast cancer molecular subtypes: functional regulation by highly connected, non-redundant micro-RNAs.Statistics of Quality and Technical Standards Networks in ColombiaAgent-Environment Mutual Information in a Potential Game ContextRed de interacciones planta-visitante floral en una comunidad de páramo altiandino (Parque Nacional Sierra Nevada, Mérida, Venezuela).Representación y análisis de los procesos de desintegración de las partículas subatómicas aplicando el formalismo del Análisis de Redes

DINÁMICAS Y NARRATIVAS DE LA GESTIÓN DE LOS ACUEDUCTOS COMUNITARIOS

Uncovering the scaling behaviour of road accidents in urban environments

Difusión en Redes Multicapa: Una Aplicación a la Transmisión de Enfermedades Infecciosas

Using Networks Science to analyze Guardiola’s Barcelona

Evolución temporal de la investigación en física, análisis bibliométrico aplicando el formalismo de las redes complejas.

Gamification for decision making analysis

Social cohesion emerging in human systems of a community-based physical activity program: Towards a planetary Health

Nonlinear effects in random walks

Multiresolución Espacial en el Análisis de Redes de Datos Complejas

Análisis automático de casos

Un modelo de la dinámica de contagio de un entomopatógeno dentro de metapoblaciones de insectos plaga para el diseño de planes de manejo regional

M I N I S Y M P O S I U M

Construction of Gene Regulatory Network using RNA Sequencing data based on graphical models

Cristian Andrés González Prieto and Liliana López-Kleine

Universidad Nacional de Colombia - Sede Bogotá

Interactions between genes, such as regulations are best represented by gene regulatory networks (GRNI) [1][2][3][4]. These are often constructed based on gene expression data. Few methods for the construction of GRN exist for RNA sequencing count data. One of the most used methods for microarray data is based on graphical Gaussian networks [5]. Considering that count data have different distributions, a method assuming RNA sequencing counts distribute Poisson has been proposed recently [1]. Nevertheless, it has been argued that the most likely distribution of RNA sequencing counts is not Poisson due to overdispersion. Therefore, the negative binomial distribution is much more likely. For this distribution, no model-based method for the construction of GRN has been proposed until now. Here, we present a graphical, model-based method for the construction of GRN assuming a negative binomial distribution of the RNA sequencing count data.

We tested our methodology using simulated RNA-Seq count data with 100 genes and 30 samples (15 with a special condition and 15 controls) and real count data from an experiment about infection of Arabidopsis thaliana. We found that negative binomial distribution fits better than Poisson distribution because overdispersion is taken into account. Goodness of fit measurements show that model are suitable. And, in real data, genes hubs are involved in defence procedure of A. thaliana.

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B I O L O G I C A L N E T W O R K S

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R E F E R E N C E S :

1. Allen GI, Liu Z. A local poisson graphical model for inferring networks from sequencing data. IEEE Trans Nanobioscience 2013;12:189-98.

2. Karlebach G, Shamir R. Modelling and analysis of gene regulatory networks. Nat Rev Mol Cell Biol 2008;9:770-80.3. Jia B, Xu S, Xiao G, Lamba V, Liang F. Learning gene regulatory networks from next generation sequencing data. Biometrics 2017;73:1221-30.

4. Liang F, Song Q, Qiu P. An Equivalent Measure of Partial Correlation Co-eficients for High Dimensional Gaussian Graphical Models. J Am Stat Assoc2015;110:1248-65.

5. Liu Z-P. Reverse Engineering of Genome-wide Gene Regulatory Networks from Gene Expression Data. Curr Genomics 2015;16:3-22. https://doi.org/doi:10.2174/1389202915666141110210634.

Long-Range Interactions in Geometric Neuronal Networks

We explore the consequences of including high-order connectivity effects in the dynamical interaction of neurons connected in a network with a spatial connectivity pattern. We adapt the formulation proposed by E. Estrada et. al. in Refs. [1–3] to a spatial network of Morris-Lecar neurons with time-dependent synaptic chemical coupling. We focus in the regime where travelling synchronization waves are observed for first-neighbour based coupling [4], to evaluate the changes induced when higher-order dynamical interactions are permitted. We observe that the travelling wave phenomenon gets enhanced by this long-range interactions, allowing the information to travel further in the system without generating pathological full synchronization states. This scheme could be a first step in a simple modelization of neuroglial networks.


Alejandro Tlaie Boria, Inmaculada Leyva Callejas and Irene Sendiña-Nadal1 12

Rey Juan Carlos University, Madrid Center for Biomedical Technology, Technical University of Madrid, Madrid1

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[1] Lopes FM, Cesar RM and Costa Lda F., Gene expression complex networks: synthesis, identification, and analysis, J. Comp. Biol., Oct 10 10, 1353-67 (2011).

[2] S. Arianos, E. Bompard, A. Carbone and F. Xue, Power grid vulnerability: A complex network approach, Chaos, Feb 19, 013119 (2009).

[3] Grandjean M., A social network analysis of Twitter: Mapping the digital humanities community, Cogent Arts and Humanities 3, (1) (2016).

[4] Leyva, I., Navas, A., Sendia-Nadal, I., Buld, J. M., Almendral, J. A., Boccaletti, S. (2011): Synchronization

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Enrichment of modular structures in biological networks: A guideline for comparison

Guillermo de Anda-Jáuregui

Instituto Nacional de Medicina Genómica

With the increasing capacity of high-throughput data generation in the biological and biomedical fields comes the need for methods for their analysis and comparison. In this regard, reconstruction and analysis of biological networks has become an important part of research in these fields.

A common workflow for analyses in network biology consists in 1) the reconstruction of a network from high-throughput biological data, or the mapping of these data to a known biological network; 2) detection of communities or clusters in such network; and 3) the association of known biological functions to said modules through an enrichment analysis.

In order to compare the results of applying such pipeline to different biological conditions in order to compare them, several considerations should be taken into account. Unfortunately, general recommendations and best practices in these regards are not readily available.

Here, critical points in the module enrichment pipelines are identified at each step. By considering these, it is possible to define the scope and limitations of the comparisons that may be performed. With these considerations in mind, quantitative metrics are proposed for comparing the results at each step: for the comparison of the network topology and composition, the modular structures, and the functional associations identified. These may serve as a guideline for biological and biomedical researchers to better present the result of their analyses.

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Construcción y comparación de redes génicas de co-expresión de genes activos en cáncer de pulmón

Michael Carvajal, Mauro Florez, Liliana López-Kleine, Daniel Osuna and Andrea Otalora

Departamento de Estadística, Facultad de Ciencias, Universidad Nacional de Colombia, Bogotá D.C., Colombia

En el siguiente trabajo se desea presentar la construcción y comparación de redes de Co-expresion de genes diferencialemnte expresados en diferentes estudios de cáncer de pulmon. Inicialmente se consideraron 14 bases que son públicas en el Centro Nacional de Información Biotecnológica (NCBI),incluyendo bases con datos de microarreglos y de RNASeq, dos técnicas de secuenciación del transcriptoma. Con cada uno de estos conjuntos de datos se realizó un preprocesamiento que consistió en sumarización, normalización y transformación, realizando además un control de calidad a los datos, con lo cual se descartaron 4, con las 10 bases restantes se hizo la identificación de los genes diferencialmente expresados con el método SAM.

La construcción de las redes de Co-expresión se hizo con base en 1955 genes que se encontraron diferencialmente expresados en por lo menos 6 de los conjuntos de datos. Una vez filtrados dichos genes en cada una de las bases se construyó la matriz de similitud ($S_k$) correspondiente a cada experimento, usando el criterio de información mutua ya que la expresión de los genes no presentaba una relación lineal. Posteriormente se define un umbral de similitud con el cual se construye cada una de las matrices de adyacencia ($A_k$) de las cuales ya se pueden obtener las redes de Co-expresións.

Finalmente para la comparación se utilizaron dos métodos: Alineamiento de redes (GEDEVO) y Common Connection Pattern (CCP). En el caso de GEDEVO se implementó un algoritmo en R que permitió encontrar parejas de genes similares entre las redes cómo también establecer el grado de similitud de los sistemas de Co-expresión, por otro lado, con CCP se pudo realizar la intersección de las redes dos a dos encontrando estructuras moleculares comunes que se pueden interpretar cómo un conjunto de genes que realizan una actividad coordinada al ser expuestos a la condición. Al contrastar los resultados de estos métodos se encontró una lista de genes que se pueden catalogar cómo importantes en el proceso biológico llevado acabo en el cáncer de pulmón, además de los componentes moleculares que permitieron ver de que manera interactúan y cómo estos mini-sistemas prevalecen en las distintas comparaciones.


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Midiendo la complejidad de los proyectos de Infraestructura de transporte vial: análisis a través de una red compleja.

El presente trabajo propone a partir de una estructura compleja de variables, modelar la gestión de proyectos de infraestructura , teniendo en cuenta las complejidades de información, planificación determinista/no determinista, aparición de novedades internas o externas que generan incertidumbres e inestabilidades, y la organización temporal. Esta estructura compleja de variables permitirá hacer el tránsito de una estructura secuencial, a partir del PMBOK® [1], a una estructura dinámica que incorpore la complejidad. Esta estructura compleja de variables permitirá profundizar en el estudio de las complejidades de proyectos de infraestructura, al analizar el nivel de planificación inicial y cómo ésta expone al proyecto a un posible fracaso. Estudios sobre proyectos de infraestructura dan cuenta que éstos se enfrentan a diferentes tipos de riesgos, condiciones climáticas cambiantes, variabilidades en productividad, ejecuciones con problemas de calidad por parte de contratistas, inestabilidad financiera, conflictos legales, costos inesperados, oposición de la comunidad, ofertas poco realistas, cambios en los diseños, obstáculos generados por permisos, entre otros. Lo anterior, indica que se hace necesario entender estas dinámicas al estudiar los proyectos de infraestructura a través de una red compleja, determinando sus complejidades.

Se toma como caso de estudio para la validación, la simulación de un proyecto de intervención vial en la zona centro occidente de Colombia, departamento de Caldas – Municipio de Palestina. Con base en una distribución uniforme discreta para el cálculo del tiempo, se obtuvieron los grados totales de la red compleja, se definieron conceptos de nodos fuertes y débiles, nodos vulnerables [4], densidad de la red, adyacencia, centralidad, conceptos de nivel de criticidad y conceptos de nivel de incertidumbre aleatoria e incertidumbre epistémica del proyecto [5].

Como conclusión final se tiene que este tipo de proyectos se planifican bajo un modelo ideal, en donde se mantienen los tres criterios críticos: tiempo, alcance y costos.

C I T I E S & N E T W O R K S : C H A L L E N G E S I N S U S T A I N A B L E U R B A N M E T A B O L I S M

Universidad Nacional de Colombia, Manizales, Colombia [email protected]

Luz Stella Cardona


1. De Rus, G., Campos J. y Numbela G.: Economía de transporte. Editor, Antoni Bosch. (2003)

4. Knight, F.: Risk, Uncertainty and Profit, Houghton Mi�in, Boston. Disponible en sitio web econlib.org. (1921)

5. Nagurney, A. y Quiang, Q.: A transportation network e�ciency measure that captures flows, behavior, and costs with applications to network component importance identification and vulnerability. In: 18th Annual POMS Conference. Dallas, Texas (2007)

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Modelado de la gestión del agua en la región hídrica Bogotá – Colombia

En la región hídrica de Bogotá se genera el 17% del PIB de Colombia y se alberga el 20% de la población del país (Dane, 2018). Durante las últimas dos décadas, los informes públicos sobre el estado del recurso hídrico en la cuenca del río Bogotá establecen que el índice de Vulnerabilidad Hídrica por desabastecimiento alcanza la calificación muy alto, mientras que estudios sobre el impacto combinado del cambio climático y aumento de la población aumentarían la demanda hasta 50% en 2050 (Huitaca consorcio - CAR, 2017; IDEAM, 2015), lo cual representa un riesgo muy alto para la economía y la población de esta parte de Colombia. Por esto se hace necesario identificar las condiciones para garantizar que el suministro de agua en la región sea sostenible en el largo plazo, mediante el uso de simulación. En las primeras simulaciones se encontró que, si las demoras en la ampliación de la oferta se sitúan entre los 5 y 9 años (ver Figura 1) y las políticas de gestión de la oferta se aplican suficientemente, aunque se tiene pocas políticas desde el lado de la demanda, no se avizora desabastecimiento, aunque con el cambio climático esta situación podría cambiar (Guhl-Nanneti, 2013). Como conclusión a pesar de que desde el lado de la oferta se observa que no tiene riesgo de desabastecimiento en los siguientes años, el cambio climático y el aumento de consumo de agua podría llevar a problemas, y hay que buscar las políticas desde el lado de la oferta y la demanda que nos ayude a mantener una ciudad sostenible.


Universidad Jorge Tadeo Lozano Politecnico Gran Colombiano1

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Sebastian Zapata Ramírez, Andrés G Chavarro, Monica Castaneda and Isaac Dyner1 2 1 1

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En el siguiente trabajo se desea presentar la construcción y comparación de redes de Co-expresion de genes diferencialemnte expresados en diferentes estudios de cáncer de pulmon. Inicialmente se consideraron 14 bases que son públicas en el Centro Nacional de Información Biotecnológica (NCBI),incluyendo bases con datos de microarreglos y de RNASeq, dos técnicas de secuenciación del transcriptoma. Con cada uno de estos conjuntos de datos se realizó un preprocesamiento que consistió en sumarización, normalización y transformación, realizando además un control de calidad a los datos, con lo cual se descartaron 4, con las 10 bases restantes se hizo la identificación de los genes diferencialmente expresados con el método SAM.

La construcción de las redes de Co-expresión se hizo con base en 1955 genes que se encontraron diferencialmente expresados en por lo menos 6 de los conjuntos de datos. Una vez filtrados dichos genes en cada una de las bases se construyó la matriz de similitud ($S_k$) correspondiente a cada experimento, usando el criterio de información mutua ya que la expresión de los genes no presentaba una relación lineal. Posteriormente se define un umbral de similitud con el cual se construye cada una de las matrices de adyacencia ($A_k$) de las cuales ya se pueden obtener las redes de Co-expresións.

Finalmente para la comparación se utilizaron dos métodos: Alineamiento de redes (GEDEVO) y Common Connection Pattern (CCP). En el caso de GEDEVO se implementó un algoritmo en R que permitió encontrar parejas de genes similares entre las redes cómo también establecer el grado de similitud de los sistemas de Co-expresión, por otro lado, con CCP se pudo realizar la intersección de las redes dos a dos encontrando estructuras moleculares comunes que se pueden interpretar cómo un conjunto de genes que realizan una actividad coordinada al ser expuestos a la condición. Al contrastar los resultados de estos métodos se encontró una lista de genes que se pueden catalogar cómo importantes en el proceso biológico llevado acabo en el cáncer de pulmón, además de los componentes moleculares que permitieron ver de que manera interactúan y cómo estos mini-sistemas prevalecen en las distintas comparaciones.


DANE. (29 de 06 de 2017). Producto Interno Bruto departamental 2005 - 2017 Base 2015. Obtenido de https://view.o�ceapps.live.com/op/view.aspx?src=http://www.dane.gov.co/files/investig aciones/pib/departamentales/B_2015/PIB_Departamentos_2017preliminar.xls

Guhl-Nanneti, E. (2013). LA REGIÓN HÍDRICA DE BOGOTÁ. Revista Academia Colombiana de Ciencias Vol 37 (144) septiembre, 327-341.

Huitaca consorcio - CAR. (2017). AJUSTE DEL PLAN DE ORDENACIÓN Y MANEJO DE LA CUENCA DEL RÍO BOGOTÁ. Bogotá: CAR.

IDEAM. (2015). Estudio Nacional del Agua 2014. Bogotá: IDEAM.

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Estudio teórico de la dinámica de propagación del VIH en una población que interactúa en una red compleja de contactos sexuales

La Teoría de Redes Complejas es un área de la matemática con bases en la teoría clásica de grafos de las matemáticas discretas. En las últimas décadas, y gracias al surgimiento de los métodos computacionales, se ha dado un desarrollo acelerado de las técnicas de análisis y se han encontrado múltiples aplicaciones a diversos problemas de la Ciencia y la Tecnología. En este trabajo se pretende mostrar la utilidad de las redes complejas para estudiar la dinámica de propagación del VIH en una población susceptible, donde los vértices de la red representan personas; adicionalmente, se incorpora en cada vértice infectado la dinámica de infección por VIH en la escala inmunológica, la cual es modelada mediante un sistema de Ecuaciones Diferenciales Ordinarias (EDO). Esta estructura multiescala del modelo permite condicionar la dinámica de propagación en la escala poblacional al estado particular inmunológico de los individuos y viceversa.


Universidad del Quindío, Armenia, Colombia. [email protected] Universidad del Quindío, Armenia, Colombia. [email protected] Universidad del Quindío, Armenia, Colombia. [email protected]

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Hernán Darío Toro-Zapata, Dennis Alexánder Prieto-Medellín, and Carlos Andrés Trujillo-Salazar1 2 3

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Tráfico urbano de cannabis modelado por una red compleja

El cannabis es la sustancia ilícita más utilizada en el mundo y representa un importante problema de salud pública. Una forma de estudiar esta problemática es mediante el análisis de la forma en que se trafica. Se propone entonces un modelo de red compleja que describa la distribución de cannabis en una ciudad, donde los nodos son barrios, localidades o sitios de expendio y mediante el estudio de diferentes propiedades estructurales de red, se establece el nodo o los nodos de mayor influencia, para luego discutir las consecuencias de su posible eliminación.


Universidad del Quindío, Armenia, Colombia. Universidad Nacional de Colombia, Manizales, Colombia. [email protected] Universidad del Quindío, Armenia, Colombia. Universidad Nacional de Colombia, Manizales, Colombia. [email protected] Universidad Nacional de Colombia, Manizales, Colombia. [email protected]

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Carlos Andrés Trujillo-Salazar, Hernán Darío Toro-Zapata and Gerard Olivar Tost1 2 3

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Assessing resilience of the Colombian Power Grid through percolation analysis

We show a resilience analysis of the Colombian Power Grid using concepts of percolation. To do so, we use the second order Kuramoto model for the dynamical study of network synchronization. Then, we perform a bond percolation approach to show how the transition to a complete blackout is achieved by elimination of most vulnerable links in the network and compare it with the transition under random removal of links. With this analysis we show that, although the Colombian Power grid is quite robust to random failures, the removal of vulnerable links decreases dramatically its resilience.


Instituto de Matemáticas Aplicadas, Grupo de Modelado Computacional /Dinámica y Complejidad de Sistemas. Universidad de Cartagena, Cartagena,ColombiaIngeniería de Sistemas, Universidad de Cartagena, Cartagena, Colombia {fdangulog,decermenopg}@unicartagena.edu.coUniversidad Nacional de Colombia Sede Manizales. Facultad de Ingeniería y Arquitectura. Departamento de Ingeniera Eléctrica, Electrónica y Computación, Percepción y Control Inteligente, Colombia {fccgalindog,gaosoriolg}@unal.edu.co

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David Angulo-Garcia, Cristian Camilo Galindo, Gustavo Osorio and David Eduardo Cermeño Pinzon1 2 33

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From centrality measures to persistent homology in metropolitan areas

We study centrality measures and persistent homology in street networks of the 74 metropolitan areas of México. The results show that betweenness centrality and Betti numbers are complementary to understand underlying patterns in cities. We propose a classification of metropolitan areas based on the number of representative 1-cycles.


CONACyT-CentroGeo-CentroMet. [email protected]

Juan Antonio Pichardo-Corpus

lanet-2019

Identification of Thefts Patterns with Space-Time Event Networks

In this proposal we submit a methodology to analyse events embedded in space and time as a complex system. In order to find patterns and take advantage of the season of the year information, we propose to construct time series of event networks. Then we seek to choose one network between different options to practical decisions. In this stage we suggest to study motifs; but, as they are masked by the number of links across time, we present our empirical random network event generator. We study the thefts in Medellín between 2003 and 2015. Among the results, it is appropriated to build networks with radius of roughly 1000 meters. Moreover, motifs are negative correlated, and for that, monthly one of them set up the crime.


Universidad Nacional de Colombia, Bogotá D.C, Colombia

Rafael Renteria-Ramos

El rol de los parasitoides en el sistema Silene-Hadena: un enfoque estructural y dinámico

El sistema Silene-Hadena es una pequeña red de interacciones ecológicas en la cual se tiene una planta Cariofilácea (del género Silene) que es polinizada de manera casi exclusiva por un lepidóptero (del género Hadena), que la utiliza a su vez como depositario exclusivo de sus huevos. Estos eclosionan y las larvas se alimentan de los frutos germinados de la planta, lo que provoca una relación ambivalente, beneficiosa y dañina para ambas especies, que oscila entre mutualismo y parasitismo.

Estos sistemas de polinización-depredación han sido descritos en hasta nueve familias de angiospermas, de modo que no se trata de un fenómeno aislado, sino de un mecanismo evolutivo que ha surgido, de manera paralela, entre varias especies. En estos sistemas, existe un balance sutil entre los beneficios (la polinización) y los costos (la depredación) que permite un balance mayoritariamente positivo para ambas especies. Sin embargo, pequeñas perturbaciones, como pueden ser la presencia de terceras especies, pueden empujar el sistema hacia uno u otro extremo.

En este trabajo presentaremos, por medio de simulaciones numéricas, un modelo dinámico de la relación entre dos especies tipo, Silene latifolia y Hadena bicruris. Estudiaremos el rol que juega en ellas la presencia de una tercera especie, de avispas parasitoides, que depredan a las larvas de Hadena. Nuestra hipótesis es que esta tercera especie ejerce un rol regulatorio sobre las poblaciones de todo el sistema y ayuda, de esa forma, a mantener el equilibrio. Presentaremos además un análisis estructural de la red de interacciones, que es usada como la base para la construcción del sistema dinámico.

C O M P L E X N E T W O R K S I N E C O L O G Y : F R O M B A C T E R I A T O M A M M A L S

Universidad del Pacífico, Lima, Peru. [email protected] Grupo de Sistemas Complejos, Universidad Politécnica de Madrid, 28040 Madrid, Spain Dep. Biología y Geología, Física y Química Inorgánica, Universidad Rey Juan Carlos-ESCET, 28933 Madrid, Spain

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2

3

Luciano Stucchi, Luiz Giménez-Benavides, and Javier Galeano1;2 3 2

lanet-2019

lanet-2019

A quick review of ecology of bacteria

Microbes are the dominants, in number and diversity, on the Three domains of life on the Earth. These organisms manage to live in almost every habitat from poles, deserts to deep sea in very different conditions. These microorganisms produce communities defined by the ecological interactions between the organisms and the environment. In particular, they make up the microbiota that we can found in and on all multicellular organisms. Nowadays, the human microbiota has been receiving much attention lately, and there is clear evidence that interplay exists between the gut microbiota and the development of diseases, as obesity, Crohns disease and ulcerative colitis, diabetes , and many others. In these talk we will review the type of interactions between populations in the microbiota. In particular, we are interested in medical problems.

C O M P L E X N E T W O R K S I N E C O L O G Y : F R O M B A C T E R I A T O M A M M A L S

Complex Systems Group, ETSIAAB, Univerisdad Politecnica de Madrid, Spain [email protected] Servicio de Microbiología del Hospital Ramón y Cajal, Madrid, Spain1

2

Javier Galeano and Rosa del Campo1 2

lanet-2019

Temporal Analysis of the Asylum Seekers Network

Animals are capable of travelling, even across continents or oceans. Humans are no exception and their flows are particularly rich given the complex social structures that generate endless opportunities to seek and dangers to run from. Time is essential to grasp the intricate mechanisms underlying macro-scale changes in the geographical distribution of populations. When cast in a network form this problem lends itself to be effectively explored using advanced techniques and powerful computers to peek into the underlying dynamics in order to extract meaningful insights. Such insights can not only be used to better understand our society but in principle can be leveraged to help mitigate the inevitable tension that arises when different culture are suddenly forced to mix, an ever increasing concern in modern times. We made an analysis of temporal correlations on the asylum seekers network in order to automatically detect sudden behavior shifts and build a website (https://migrationets.github.io/MigrationFlows/) to understand and raise awareness.

C O M P U T A T I O N A L M A S S M I G R A T I O N S : B I G D A T A T O U N D E R S T A N D M O B I L I T Y

The University of Vermont, Department of Computer Science, Burlington, VT, USAThe University of Vermont, Complex Systems Center, Burlington, VT, USAThe University of Vermont, Department of Mathematics and Statistics, Burlington, VT, USA{olangle,lfrati,ncheney,pdodds}@uvm.edu

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3

Ollin Demian Langle Chimal, Lapo Frati, Nick Cheney, and Peter S. Dodds1;2 1;2 1;2 2;3

lanet-2019

Graph Database for Analysis and Modelling of Microgrids based on Complex Networks Theory

N E T W O R K P R O P E R T I E S O F T O P O L O G I C A L D A T A A N A L Y S I S

Network science focuses on analyzing networks structure in order to understand key relational patterns in complex systems. On the other hand, electrical power microgrids are networks with non-trivial topological features, that can be described as a set of interacting elements of different nature, such as energy storage devices, energy transformers, loads, and monitoring and protection devices from distributed generation components [1] [2] [3]. Thus, electrical power microgrids can be categorized as complex networks and analyzed through the lens of network theory and profit from its properties to identify emerging behaviors such as vulnerability, stability and robustness and dynamic response, inherent all them to topological features of the network [4]. Considering the complexity of power networks, metrics of simple graphs as the impedance node graph model commonly used in power system analysis, cannot provide significant information since they fail to identify and explicitly incorporate, not only the physical laws, but also social, economic and environmental behavior, that impacts the power system, having an undeniable effect on power ow, energy availability, costs and sustainability of the power system [5][6]. This work outlines some of the environmental and economic properties that we considered should be incorporated in addition of the traditional electrical properties used when representing a power grid model. This work also presents the advances in the implementation of a graph model considering the mentioned additional properties as compared to a traditional impedance node electrical model. An example of the proposed modeling strategy applied to a power grid network will be presented as well as the graph elements capability and features, and the use of graph database tools for their implementation. In figure 1 presents a microgrid impedance model, which is complemented by a graph database integrating additional information such as components characteristics, limiting conditions, and network data availability, among others.

Universidad Antonio Nariño. Centro de Investigaciones en Ciencias Básicas y Aplicadas (CICBA) Bogotá, Colombia {angreyes,rafael.gutierrez,andres.hernandez}@uan.edu.co

Angie K. Reyes, Rafael Gutierrez and Andres Hernandez


1. Sinan G Aksoy, Emilie Purvine, Eduardo Cotilla-Sanchez, and Mahantesh Halappanavar. A generative graph model for electrical infrastructure networks. Journal of Complex Networks, 7(1):128–162, 2018.

4. Mariya Soshinskaya, Wina HJ Crijns-Graus, Josep M Guerrero, and Juan C Vasquez. Microgrids: Experiences, barriers and success factors. Renewable and Sustainable Energy Reviews, 40:659–672, 2014.

5. Ettore Bompard, Roberto Napoli, and Fei Xue. Analysis of structural vulnerabilities in power transmission grids. International Journal of Critical Infrastructure Protection, 2(1-2):5–12, 2009.

lanet-2019

Multi-scale Anomaly Detection on Attributed Networks

Anomalous data can be understood as the noteworthy objects with patterns or behaviors that deviate significantly from any background property. Moreover, many real life problems can be modeled as complex networks having attributes associated with the nodes. For instance, social networks might have demographic information on the nodes, co-purchase networks might contain information of articles on the nodes, etc. Because the mechanisms generating anomalies in real world networks are usually unknown, measuring the outlierness of nodes is a challenging task, constrained by the network structure and heterogeneity of the nodal metadata.Although anomaly detection methods abound, there is a lack of consensus on how to quantify and rank anomalies on networks. Here we present a novel framework providing a simple physical interpretation for anomalous nodes in attributed networks: they are nodes with highly concentrated energy in a diffusive process taking place on the network. Our framework provides a simple and principled mechanism to rank and localize outliers at different scales.We address this problem under the lens of the graph signal processing framework by considering nodes attribute as signals in the vertex domain. This allows us to couple nodal information with graph structure. By looking at the spectral properties of the graph, the harmonic behavior of the graph signal can be investigated. When a heat kernel operator describing a diffusive process on the network is used as a low-pass filter, our approach allows us to combine graph signal processing with the framework of graph partition stability.Tests on real world benchmarks show that our method allows to not only recover the so called ground truth anomalies, but also to discover new anomalies jointly with their context. Our work shows that anomaly detection on networks requires a principled investigation considering simultaneously the anomalies and the context in which they are relevant.


Institute for Information and Communication Technologies , Electronics and Applied Mathematics (ICTEAM), Université catholique de Louvain, BelgiumnaXys and Department of Mathematics, Université de Namur, BelgiumCenter of Operations Reserach and Econometrics (CORE), Belgium {firstname,lastname}@uclouvain.be

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23

Leonardo Gutiérrez Gómez, Alexandre Bovet and Jean-Charles Delvenne1 1;2 1;3

lanet-2019

Network Embeddings For Graph Classification, Clustering and Visualization

A main challenge in mining network based data is finding effective ways to represent or encode graph structures so that it can be e�ciently exploited by machine learning approaches. Several methods have focused in network representation at node/edge or substructure level. However, many real life challenges such as time-varying, multilayer, chemical compounds and brain networks involve groups of graphs opening additional challenges in graph comparison and representation. Traditional approaches for learning representations relies on hand-crafting specialized heuristics to extract meaningful information about the graphs, e.g statistical properties, structural features, as well as engineered graph distances to quantify dissimilarity between networks. Here we provide an unsupervised approach to learn embedding representation for input graphs so that it can be used in numerous graph mining tasks. By using an unsupervised neural network approach on input graphs, we learn a smaller dimensional representation which is used to compare graphs in a Euclidean space. Our method is tested empirically on synthetic and real life datasets and evaluated on graph visualization and classification tasks. Results reveal that our method outperform well known graph-kernels in classification tasks being highly e�cient.


Uniersité catholique de Louvain

Leonardo Gutiérrez Gómez and Jean-Charles Delvenne

Anomalous data can be understood as the noteworthy objects with patterns or behaviors that deviate significantly from any background property. Moreover, many real life problems can be modeled as complex networks having attributes associated with the nodes. For instance, social networks might have demographic information on the nodes, co-purchase networks might contain information of articles on the nodes, etc. Because the mechanisms generating anomalies in real world networks are usually unknown, measuring the outlierness of nodes is a challenging task, constrained by the network structure and heterogeneity of the nodal metadata.Although anomaly detection methods abound, there is a lack of consensus on how to quantify and rank anomalies on networks. Here we present a novel framework providing a simple physical interpretation for anomalous nodes in attributed networks: they are nodes with highly concentrated energy in a diffusive process taking place on the network. Our framework provides a simple and principled mechanism to rank and localize outliers at different scales.We address this problem under the lens of the graph signal processing framework by considering nodes attribute as signals in the vertex domain. This allows us to couple nodal information with graph structure. By looking at the spectral properties of the graph, the harmonic behavior of the graph signal can be investigated. When a heat kernel operator describing a diffusive process on the network is used as a low-pass filter, our approach allows us to combine graph signal processing with the framework of graph partition stability.Tests on real world benchmarks show that our method allows to not only recover the so called ground truth anomalies, but also to discover new anomalies jointly with their context. Our work shows that anomaly detection on networks requires a principled investigation considering simultaneously the anomalies and the context in which they are relevant.

The influence of EEG references on the analysis of spatio-temporal interrelation patterns

The characterization of the functional network of the brain dynamics has become a prominent tool to illuminate novel aspects of brain functioning. Due to its excelent time resolution, such research is oftentimes based on electroencephalographic recordings (EEG). However, a particular EEG-references might cause crucial distortions of the spatio-temporal interrelation pattern and may induce spurious correlations as well as diminish genuine interrelations originally present in the dataset. Here we investigate in which manner correlation patterns are affected by the EEG reference. To this end we probe the influence of 7 popular reference schemes on artificial recordings derived from well controlled numerical test frameworks.

In this respect we are not only interested in the deformation of spatial interrelations, but we test aditionally in which way the time evolution of the functional network, estimated via some bi-variate interrelation measure, gets distorted. It turns out that the median reference shows best performance in most situations considered in the present study. However, if a collective brain dynamics has to be expected, where most of the signals get correlated, also this scheme may cause crucial deformations of the functional network, such that the parallel use of different reference schemes seems advisable.


Universidad Nacional Autónoma de México Universidad Autónoma del Estado de Morelos1

2

Wady Alexander Ríos Herrera, Markus Müller and Ana Leonor Lopez Rivera12

lanet-2019

lanet-2019

The Topology of Communicating Across Cities of Increasing Sizes, or the Complex Task of ”Reaching Out” in Larger Cities

Cities have been compared to social reactors constrained by the communication and coordination possibilities offered by an urban environment that has only grown since the advent of the industrial age. We attempt to provide a first description of human interactions in the urban environment using Call Detailed Records (CDR) of the major mobile phone communication network operator in Chile. We build the communication networks for 145 cities to describe, the communication behavior of urban dwellers. Hence, our analysis consists of observed social interactions, such as number of contacts, number of calls and total communication time. We evaluate their scaling relationship with the number of mobile phones assigned to each city as an approximation of city size.

The values of scaling exponents closely match recent explanations proposed in the literature and show small average path length, i.e. small-world. However, they decrease instead of growing when size is taken into account. Different transitivity indices show mixed results. Average Watts-Strogatz clustering coe�cient increases in larger cities more than expected by pure chance as it has been shown elsewhere. On the other hand, the fact that classic transitivity index decreases seem to indicate a regime change with a decreasing relation with size and unexpected growth in larger cities.

These results indicate that while tightly knit human communities seem to lose cohesion as they grow, community properties may progressively disappear among the three to four largest urban centers in Chile where the coordination of complex functions require city dwellers to reach out to a larger network of people while investing larger periods on the phone, compared to smaller cities. Finally, although these results are valid for all networks, we observe two distinct regimes at a critical network size of ~10,000 nodes, that may mark the transition toward a proper urban environment in Chile.


Laboratorio de Ecoinformática, Universidad Austral de Chile, Valdivia, Los Ros, Chile Instituto de Ecología y Biodiversidad [email protected]

2

Horacio Samaniego1;2

I G N I T E T A L K S

lanet-2019

Cities have been compared to social reactors constrained by the communication and coordination possibilities offered by an urban environment that has only grown since the advent of the industrial age. We attempt to provide a first description of human interactions in the urban environment using Call Detailed Records (CDR) of the major mobile phone communication network operator in Chile. We build the communication networks for 145 cities to describe, the communication behavior of urban dwellers. Hence, our analysis consists of observed social interactions, such as number of contacts, number of calls and total communication time. We evaluate their scaling relationship with the number of mobile phones assigned to each city as an approximation of city size.

The values of scaling exponents closely match recent explanations proposed in the literature and show small average path length, i.e. small-world. However, they decrease instead of growing when size is taken into account. Different transitivity indices show mixed results. Average Watts-Strogatz clustering coe�cient increases in larger cities more than expected by pure chance as it has been shown elsewhere. On the other hand, the fact that classic transitivity index decreases seem to indicate a regime change with a decreasing relation with size and unexpected growth in larger cities.

These results indicate that while tightly knit human communities seem to lose cohesion as they grow, community properties may progressively disappear among the three to four largest urban centers in Chile where the coordination of complex functions require city dwellers to reach out to a larger network of people while investing larger periods on the phone, compared to smaller cities. Finally, although these results are valid for all networks, we observe two distinct regimes at a critical network size of ~10,000 nodes, that may mark the transition toward a proper urban environment in Chile.

La inmersión de grafos es un problema en la ciencia de datos que busca representar un grafo G dándole a cada uno de sus nodos unas coordenadas en un espacio de baja dimensión. Esta representación luego puede ser usada para diferentes tareas de “graph mining”, como por ejemplo la reconstrucción del grafo y predicción de aristas. Un algoritmo muy conocido de inmersión de grafos es el llamado "Laplacian Eigenmaps", el cual construye una inmersión sobre la base de las propiedades espectrales de la matriz Laplaciana de G. La intuición detrás de este algoritmo, así como de muchos otros, es que la inmersión de un grafo debe respetar la similitud de sus nodos. Es decir, si dos nodos son similares, entonces sus coordenadas deben quedar a una distancia próxima. En este trabajo, nos deshacemos de esta heurística. En vez de ella, utilizamos la matriz Laplaciana para construir una inmersión con propiedades tanto geométricas como espectrales, aprovechando la llamada geometría simpléctica de G. Con esto, presentamos un nuevo algoritmo llamado "Geometric Laplacian Eigenmap Embedding” (abreviado GLEE), y demostramos que tiene un mejor desempeño que otros algoritmos de inmersión (incluido el original "Laplacian Eigenmaps”) en las tareas de reconstrucción y predicción de aristas. También demostramos que GLEE es robusto al ruido, ya que es capaz de recuperar la estructura del grafo subyaciente incluso en la presencia de un alto porcentaje de aristas ocultas. Al concentrarnos en la geometría del espacio de inmersión, este trabajo presenta un punto de vista alternativo a la literatura actual en inmersión de grafos para la ciencia de datos.

G L E E : G E O M E T R I C L A P L A C I A N E I G E N M A P E M B E D D I N G

Network Science Institute, Northeastern University, Boston MA 02115, USA. [email protected] Khoury College of Computer Sciences, Northeastern University, Boston MA 02115,USA. [email protected]

2

Leonardo Torres1 and Tina Eliassi-Rad1;21

lanet-2019

Understanding how networks arrange their connections (structure), and how the information flows through their nodes (dynamics), is a breakthrough for many scientific fields that rely on network science to evaluate nature phenomena. Recently, Information Theory methods gained the spotlight in an attempt to create a more quantitative and robust characterization of complex networks, as an alternative to traditional methods.

In this work, we propose a characterization of complex networks, considering their topological structure and dynamics when evaluating a random-walk-based probability distribution per node and the averaged information theory quantifiers: Network Entropy and Fisher Information Measure.

We studied the following synthetic networks: Erd\H{o}s-R\ényi (ER); Watts-Strogatz(WS); and Barabási-Albert with nonlinear preferential attachment (nonlinear PA), observing how they perform in the Shannon-Fisher plane, pointing out expected behaviors and extending this analysis to comprise real networks, then, comparing and discussing our results with existing methods for analyzing networks.

C H A R A C T E R I Z I N G C O M P L E X N E T W O R K S U S I N G I N F O R M A T I O N T H E O R Y Q U A N T I F I E R S

LaCCAN/CPMAT, Instituto de Computacao, Universidade Federal de Alagoas. { cristopher, alla, acfrery }@laccan.ufal.brDepartamento de Ciencia da Computacao, Universidade Federal de Minas Gerais. [email protected] de Física, Universidade Federal de AlagoasHospital Italiano de Buenos Aires, CONICET, Argentina. [email protected]

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Cristopher De Sousa Freitas, Andre Aquino, Heitor S. Ramos, Alejandro C. Frery and Osvaldo Anibal Rosso

1 21

1 1,3,4

lanet-2019

Mobility in science is a network-based phenomena. In addition to the clearly identifiable source and destination points, which together facilitate the analysis of high-skilled brain-drain [1,2], the role played by prior collaboration channels in mediating future relocation opportunities is another network-based feature [3]. In this talk I will discuss recent work estimating the impact of researcher mobility in science using the Rubin potential outcomes framework [4] implemented via the propensity score matching (PSM) method [5]. While recent work identified a 17% increase in citations for mobile researchers relative to their non-mobile counterparts using this PSM method [3], this talk will focus on measuring the comparative advantage associated with researcher mobility trajectories that book the full round trip. Such “home return” occurs when the source location for a particular researcher’s initial mobility event coincides with the destination location of a subsequent mobility event. In particular, our estimation relies on matching researchers with return-mobility trajectories to their closest counterfactual outcome - mobile researchers sourced from the same home location but without return-mobility trajectories. The PSM method produces optimal matches by accounting for out-of-sample researcher profile characteristics, and is thus particularly amenable to “big data” contexts where the pool of candidate matches is su�ciently large, thereby facilitating robust causal estimation. The results of this analysis will be framed in terms of implications for immigration and national innovation system policy.

E T G O E S H O M E : Q U A N T I F Y I N G T H E R E T U R N S T O R E T U R N M O B I L I T Y

Department of Management of Complex Systems, Ernest and Julio Gallo Management Program, University of California

Alexander Petersen

lanet-2019

Living neuronal circuits are embedded in an environment that combines two-dimensional (2D) and three-dimensional (3D) organization, a layout that facilitates a rich repertoire of activity, robustness and flexibility. To understand the principal actors that shape these features, in our laboratory we develop tools to engineer neuronal circuits in vitro in both 2D and 3D. We observed that rich activity and functional characteristics can be attained by guiding the physical blueprint of neurons and connections. We also observed that functional organization can swiftly change in response to small changes in connectivity, driving the network towards highly segregated or highly integrated states. The combination of experiments, in silico modeling and network analysis provides a rich exploratory platform to understand the link between structure and function, as well as to comprehend the intricacy of the integration--segregation balance in the brain.

F U N C T I O N A L C O N N E C T I V I T Y I N E N G I N E E R E D N E U R O N A L C U L T U R E S : C H A L L E N G E S A N D O P P O R T U N I T I E S

Departament de Física de la Matéria Condensada, Universitat de Barcelona, Barcelona 08028, Catalonia, Spain Universitat de Barcelona Institute of Complex Systems (UBICS), Barcelona, Catalonia, Spain. [email protected] WPI-Advanced Institute for Materials Research (WPI-AIMR), Tohoku University, Sendai 980-8577, Japan

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Marc Montalà, Estefanía Estévez-Priego, Adriaan A. Ludl, Hideaki Yamamoto and Jordi Soriano31,2 1,2 1,2 1,2

lanet-2019

There is increasing awareness that our understanding of the dynamics and stability of human systems cannot be uncoupled from the environmental and ecological networks where these societies live, and that the resilience and robustness to change of ecosystems/environments cannot be uncoupled from human systems. Research on what is variously called socio-ecological systems, coupled human and natural systems, and human-environment systems has occurred for decades. Much of the work in this domain focuses on extant socio-ecological systems. Yet there is an enormous amount of archaeological knowledge, accumulated over more than a century of intensive research that can deeply inform, enhance, and transform our understanding of socio-ecological complex networks and interaction.

Here I present research that synthesizes data across multiple archaeological cases worldwide; this project, the ArchaeoEcology Project, builds human-centered complex ecological networks (Figure 1b), framing the past as records of experiments with sustainability that can have strong lessons for today. I focus on assessing the human place in the network—do they have low path lengths helping knit ecosystems together, or are they more peripheral (Figure 1d)? Do humans invade ecosystems and cause extinction cascades, or do they provide complex ecosystems services benefiting biodiversity? These, and other metrics, can help assess the myriad ways that humans impact ecosystems. By comparing these metrics across multiple systems I am able to paint a picture of human impacts on global ecosystems.

Further, by focusing on past systems where we know the trajectory of the society (and the ecosystems they inhabited) we can address overall resilience, stability and robustness of the system in response to multiscale natural and anthropogenic change. This work finally provides tools for moving beyond the archaeological past and assessing modern complex human-ecosystem network interactions, and suggests ways that this work can interface with existing social network analyses in archaeology.

W H A T I S T H E H U M A N P L A C E I N E C O L O G I C A L N E T W O R K S ?

The Santa Fe Institute, Center for Research and Interdisciplinarity, & Utah State University

Stefani Crabtree

lanet-2019

The United Nations Convention against Corruption (UNCAC-2003) acknowledges corruption as a transnational phenomenon that affects all societies in deep and multiple ways, at political, economic, ecological and social fronts. Therefore, comprehensive and multidisciplinary approaches are then required to prevent and combat the complexity of corruption effectively. In this work, we make use of network science to study two of the most important documented cases of political corruption in Mexico of the last decade: The ``Estafa Maestra'' (Master Fraud) and the ``Duarte-Veracruz'' phantom companies. In those cases, government agencies, public universities, shell companies, and hundreds of people within private and public sectors got involved in complex corruption.

Working with very detailed sets provided by the leading journalists in each case, we explore the complexity of the so-called corruption networks via their centrality measures, topological structure and their time-evolution. First, we show that indeed, these corruption networks go beyond allegories in the political discourse to become clear, real and concrete structures that emerge from very simple mechanisms, that is, the fulfillment of the legal requirements needed to establish contracts with the federal or local governments. Second, we explore the role of the companies-people relationship in a bipartite approach in order to find the practical and fundamental aspects that might be able to help in the prediction of future events or in the establishment of risk parameters. This is done by considering two types of nodes (companies and people), and five types of relationships/edges (administrators, legal representatives, share-holders, commissioners, and notaries) and by looking at some centrality measures and the topological structure of the corresponding projected networks according to node type and edge type and as function of time as well. Third, by looking at the very few works on the literature on corruption networks and other crime networks, some commonalities and differences among cases are discussed. Finally, we discuss further some fundamental aspects of the corruption phenomenon (such as problems in conceptualization, quantification, modeling and control), and how/why it must be studied empirically from the perspective of complexity science, and in particular, complex networks theory.

T H E C O M P L E X I T Y O F P O L I T I C A L C O R R U P T I O N N E T W O R K S

Institute of Legal Research, National Autonomous University of Mexico (UNAM), MexicoInstitute of Information and Communication Technology, University of Sindh, PakistanNational Institute of Genomic Medicine, MexicoInstitute for Research in Applied Mathematics and Systems, UNAM, MexicoObservatory of Corruption and Impunity, UNAM, Mexico

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J. R. Nicolás-Carlock, A. W. Mahessar, J. Espinal-Enríquez, G. Ramos-Fernandez, J. M. Siqueiros-García, F. Navarro-Luna, I. Luna-Pla

1;5 1;2;5 3;5 4;5

1;5 1;54;5

P A R A R E L L S

There is increasing awareness that our understanding of the dynamics and stability of human systems cannot be uncoupled from the environmental and ecological networks where these societies live, and that the resilience and robustness to change of ecosystems/environments cannot be uncoupled from human systems. Research on what is variously called socio-ecological systems, coupled human and natural systems, and human-environment systems has occurred for decades. Much of the work in this domain focuses on extant socio-ecological systems. Yet there is an enormous amount of archaeological knowledge, accumulated over more than a century of intensive research that can deeply inform, enhance, and transform our understanding of socio-ecological complex networks and interaction.

Here I present research that synthesizes data across multiple archaeological cases worldwide; this project, the ArchaeoEcology Project, builds human-centered complex ecological networks (Figure 1b), framing the past as records of experiments with sustainability that can have strong lessons for today. I focus on assessing the human place in the network—do they have low path lengths helping knit ecosystems together, or are they more peripheral (Figure 1d)? Do humans invade ecosystems and cause extinction cascades, or do they provide complex ecosystems services benefiting biodiversity? These, and other metrics, can help assess the myriad ways that humans impact ecosystems. By comparing these metrics across multiple systems I am able to paint a picture of human impacts on global ecosystems.

Further, by focusing on past systems where we know the trajectory of the society (and the ecosystems they inhabited) we can address overall resilience, stability and robustness of the system in response to multiscale natural and anthropogenic change. This work finally provides tools for moving beyond the archaeological past and assessing modern complex human-ecosystem network interactions, and suggests ways that this work can interface with existing social network analyses in archaeology.

lanet-2019

A topological model of a neuron-glia network: hypergraph and natural structures approach

Connectomics already has traced the connections of many nervous structures, performing the interpretation of their function. However, if we analyzed with few mathematical tools, we would lose information. To generate new mathematical tools, it is necessary that models grasp better the complexity of the nervous system. This study has two goals: a) make a neuron-glia network model compatible with existing mathematics theories and b) propose a new tool for connectomics. To modeling, firstly, we have defined two sets: a set "X" which includes a finite number of neurons (N) and glial cells (G), and a set "S" which includes synapsis. The last set contains three sets: tripartite synapsis, glia-glia interaction, and one glia-one neuron interaction. Secondly, from the first type of connection, we have built a distance rule D which relates two neurons by the number of neurons that are in the shortest walk which connect both in the network. We have created a metric space defined as an ordered pair (N, D). Finally, we propose a way to find hidden patterns in the network using a shape matrix created from the distance rule. The nature of the model could be in essence a hypergraph being vertices neurons and glial cells, and hyperedges as the set of synapsis which could be multiple as in the case of tripartite synapsis. Also, the neural component could be a digraph for the direction of its connections, and the glial cell component could be a simple graph. On the other hand, this model can be a natural structure where the synapsis is the NE-connection and the distances rule, the distribution. In conclusion, we have built a model which consider the main kind of interactions present in the nervous system and facilitate the generation of mathematic constructs as distance or shape.


Marco Peña-Garcia, Nelson Castro and Walter Cabrera-Febola

Universidad Nacional Mayor de San Marcos, Perú.

lanet-2019

Functional connectivity in 3D cultured neuronal networks.

We investigated activity and functional connectivity in three-dimensional (3D) neuronal cultures grown in hydrogels, a synthetic environment that mimics natural extracellular matrix. Neurons were monitored through different techniques to obtain their activity patterns with single cell resolution. Activity was then analyzed using time--delays and transfer entropy to render the functional organization of the network. We observed that 3D neuronal networks exhibit a richer repertoire of dynamics than their 2D equivalents, and that activity -and thus the traits of the functional networks- could be followed along several days in vitro.


Estefania Estevez-Priego, Adriaan Ludl, Gustavo Castro, Daniel Tornero, Anastasia Koroleva, Pablo Loza-Alvarez and Jordi Soriano

1;2 1;2

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Departament de Física de la Matéria Condensada, Universitat de Barcelona, Barcelona 08028, Catalonia, Spain Universitat de Barcelona Institute of Complex Systems (UBICS), Barcelona, Catalonia, Spain ICFO – Institut de Ciències Fotòniques, The Barcelona Institute of Science and Technology, Castelldefels, Spain Nanotechnology Department, Laser Zentrum Hannover e.V., Hannover, Germany

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lanet-2019

Fast Correlation-Based Filter algorithm andbuilding of supervised co-expression networks

Co-expression networks are one of the workhorses of systemsbiology. Current methodologies, such as WGCNA, have provided insightsinto the biology of co-expression, but a few limitations remain. Namely,even though the studies usually have labels for the samples (disease xhealth, or cell A x cell B) the information is not used for the constructionof the networks. The detection of genes of interest regarding those condi-tions relies solely on the post hoc methods of network analysis describedbefore.In this work, we present the adaptation of an information-theory basedfeature selection algorithm, FCBF (Fast Correlation-Based Filter) forsupervised co-expression network building. By harnessing the conceptof predominant correlations as module-specific essential features. Thecreated modules contain genes that are (1) correlated to the labels abovea certain threshold and (2) more correlated to other members than tothe class.We developed a supervised discretization algorithm for pre-processingof single-cell data. Using the best algorithm for each gene, this pre-processing pipeline enables the capture of different modalities of gene-expression distribution. The discretized gene expression table is corre-lated to the classes, which, in the single-cell datasets analyzed, corre-sponded to clusters determined by the Louvain algorithm. All genes arecorrelated to the class by Symmetrical Uncertainty (SU), a normalizedversion of Information

Gain. The final co-expression network has onlygenes that are su�ciently correlated to the labels.FCBF generates a list of genes predominantly correlated to the class(See Yu and Liu, 2003 for details). These predominantly-correlated genesare seeds for the FCBF modules, which, in our observations, containedgenes functionally related to one another and known markers of differ-ent cell populations. To sum up, FCBF can be used to build supervisedco-expression networks in single-cell RNA-Seq data, providing a con-ceptual stepping stone for information-theory approaches to understand co-expression networks.


Tiago Lubiana and Helder Nakaya

Department of Clinical and Toxicological Analyses, School of Pharmaceutical Sciences, University of So Paulo, So Paulo, Brazil [email protected]

lanet-2019

Assessment of Gene Regulatory Network Inference Algorithms Using Monte Carlo Simulation

Gene regulatory networks are biological models that aim to encode causal relationships among all genes belonging to an organism, which are collectively known as the organism’s genome. The large size of any given genome and the di�culty of conducting relevant experiments makes the inference of gene regulatory networks a di�cult problem from a statistical point of view. In this context, a variety of procedures have been developed to build such networks using so called genomic transcription data, which constitute the primary source of information for this task. While many of these procedures are based on algorithms from statistics and machine learning, there is insu�cient theoretical or empirical support for their correctness for the problem at hand, neither under assumed probabilistic models nor in real biological applications. In this work, we consider the problem of assessing the statistical properties of several estimation procedures found in the literature, evaluating the resulting networks with topological and statistical criteria. We approach this question by means of a Monte Carlo simulation study, positing different types of data generating processes. In particular, we focus on data generating processes with underpinning Bayesian networks, which provide a syntax to represent causal systems equivalent to structural equations models.


Adrian Zuur and Liliana López-Kleine

Department of Statistics, Universidad Nacional de Colombia, Bogotá, Colombia {agzuurp,llopezk}@unal.edu.co

lanet-2019

Phenotypes are generated by information processing systems of varying complexity. Most of these systems are characterized by their robust response to perturbations and high evolvability potential. Even small information processors produce phenotypes that are far from trivial. Some of the most studied minimal systems producing complex phenotypes are 3-node gene regulatory networks (GRN). Such networks are able to generate specific and conspicuous patterns of gene expression along a tissue or a culture of cells.These patterns are found in processes such as cell fate specification in biological development. From all possible 3-node GRNs, some achieve the same phenotype even being significantly different in structure.

In this study we describe how a large population (~2000) of different 3-node GRNs, obtained by a genetic algorithm, responds to perturbations in the gene expression rate parameters associated to each node. Every GRN is embedded in a unidimensional array of identical GRNs simulating a computational tissue. And the target phenotype for the evolutionary algorithm was a middle strip pattern of gene expression for one of the GRN nodes. Expression rates depend on the dynamics of gene products, i.e. product synthesis, diffusion and degradation. We perturbed every set of parameters describing these rates under changing initial conditions. We observed different GRN responses, some attained the same mid strip pattern in almost every perturbed instance and others tended to reach new phenotypes or to not reach any. We then linked perturbation response and topological structure by creating a genotype space where structurally similar GRN topologies are connected in a graph. We obtained a high correlation between network structure and response to perturbations."

Universidad de Antioquia, Colombia Ghent University, Belgium1

2

Sebastian Espejo, Boris Rodriguez, Jayson Gutierrez and Gloria Machado21 11

Minimal gene regulatory network robustness analysisB I O L O G I C A L N E T W O R K S

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Since the creation of the United States National Park Service (NPS) a century ago, Protected Areas (PA) have been a model for natural resources stewardship. The PA system have embodied significant lands of spectacular scenery and cultural heritage for passive enjoyment of visitors [1]. The history of the US parks has lead conservation outcomes and it has echoed in the park systems of other countries who have adopted this model. This is the cumulative result of political bargaining and managerial solutions rooted in the knowledge of administrators and policy makers. The Leopold Report (LR) is probably when science gained momentum in parks [2]. LR showed how managerial practices impacted parks scenery and highlighted that scientists should contribute in the stewardship of parks. Leopold´s idea aimed to embrace science for improving management in parks. Notwithstanding, subsequent reports claim that this transition was not completely successful.

Academia and other sectors have produced knowledge about effective managerial practices and decision making. That in principle plays a major role in natural resources management, nonetheless there are gaps between science and parks. Lacks of capabilities and asymmetries constitute part of those gaps [2, 3]. These issues were expected to be overcome after the LR, however this was lengthy. After the LR at least four science related policies in parks have been approved to reduce such gap, even though their impacts are not clear.

We study the dynamics of scientific knowledge in the NPS. This study is based upon research outputs published during last 3 decades contained in the Web of Science (WOS). We assessed the dynamics of number of publications, topic diversity and asymmetries in collaboration for different scales of aggregation (Global, USA, NPS) using a network approach."

University of California - Merced, School of Engineering, Environmental Systems Program [email protected]* University of California - Merced, School of Engineering, Ernest and Julio Gallo Management Program, Department of Management of Complex Systems

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Felber Arroyave, Alexander M. Petersen and Jeffrey Jenkins2 21;2

SCIENCE IN THE NATIONAL PARKS: DYNAMICS ASSOCIATED WITH US POLICYC L I M A T E & E C O

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1. Dilsaver, Lary M. ”Research perspectives on national parks.” Geographical Review 99.2 (2009): 268-278. 2. Parsons, David J. ”Supporting basic ecological research in US national parks: Challenges and opportunities.” Ecological Applications 14.1 (2004): 5-13. 3. Halvorson, William Lee, and Gary E. Davis, eds. Science and ecosystem management in the national parks. University of Arizona Press, 1996.

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EVALUATION OF CLIMATIC MULTIDIMENSIONAL COORDINATION IN THE WESTERN US

Climatic variability and synchronization can be driven for factors in many scales. Local, meso and macro scales affect the realization of climatic conditions producing large spatial patterns. The identification of those patterns and their drivers is relevant for understanding climatic processes and for developing predicting skills. Although local patterns and global patterns such as spatial embeddedness and El Niño Southern Oscillation (ENSO), respectively, are largely studied [1]; meso-scale phenomena are less known.

Meso-scale phenomena occur mainly in regional spatial scales but could extend their effects into larger ones as consequence of the local-global interacting phenomena. This intricate connection reveal some of the inner complexities of clime. For instance, climatic synchronization and teleconnection in space are the result of local-global interplaying processes [2,3]. Previous works have assessed in which extend clime realization is driven by meso-scale processes, however most of them have addressed these phenomena by using just one single weather variable and ignoring the interactions between different variables that account for clime realization.

In this regard, we aim to identify some meso-scale patters in a multivariate (temperature precipitation) space in order to identify highly synchronized or coordinated regions. We used the time-series for temperature and precipitation in the western of US at 1.25° resolution and for the 1918-2014 timeframe window. Synchronization was evaluated using a two-dimensional correlation (Canonical Correlation) between temperature and precipitation for each pair of pixels. Synchronization between pixels was represented as a correlation network. The network was optimized in such a way spatial emdeddedness be removed and medium-long range synchronization will be maximized. Optimization of the network will be carried out as a function of modularity. We identified several regions of climatic coordination that in a broad sense correspond to previous categorizations of climatic regions of the western of US.

Felber Arroyave and Alexander Petersen

University of California - Merced, School of Engineering, Environmental Systems

C L I M A T E & E C O

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1. Gershunov, A. (1998). ENSO influence on intraseasonal extreme rainfall and temperature frequencies in the contiguous United States: Implications for long-range predictability. Journal of Climate, 11(12), 3192-3203. 2. Ebert-Upho�, I., Huang, W., Mitra, A., Cooley, D., Chatterjee, S., Chen, C., Wang, Z. (2018). Studying extremal dependence in climate using complex networks. In Proceedings of the 8th International Workshop on Climate Informatics (CI 2018), Boulder, CO.

3. Zhou, D., Gozolchiani, A., Ashkenazy, Y., Havlin, S. (2015). Teleconnection paths via climate network direct link detection. Physical review letters, 115(26), 268501.

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While natural disasters represent a major disruptive event they are also motors for the emergence of cooperation. In recent years social media data has been used to assess damage by natural disasters and as a central part of social coordination. Here we present a study of the latest major earthquake in Mexico City (September 19 2017) using data from multiple sources in order to gain insights in the immediate effects and the collective memory, finding that the recovery time of physical patterns is lower than the emotional by returning to average mobility patterns within two weeks while still on the collective memory for almost a year.

The University of Vermont, Department of Computer Science, Burlington, VT, USA The University of Vermont, Department of Mathematics and Statistics, Burlington, VT, USA The University of Vermont, Complex Systems Center, Burlington, VT, USA {olangle,pdodds,ncheney}@uvm.edu

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Ollin Demian Langle Chimal, Peter S. Dodds, and Nick Cheney2;3 1;31;3

19S – MEXICO’S RESILIENCE IN AN EARTHQUAKE EVENC L I M A T E & E C O

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Network analysis of El Niño

Changes in climatic variables such as sea surface temperature, pressure and wind force affect the ocean that responds to the atmosphere. This atmosphere-ocean feedback is associated with internal waves in the ocean. The dynamics of the ocean wave play an important role in the dynamics of El Niño. El Niño is a climatic phenomenon that consists of the abnormal heating of the waters of the Pacific Ocean that causes changes in the circulation of the atmosphere, causing climatic changes such as droughts and floods in different parts of the globe. The Earth's climate system has many different dynamic processes operating on a space-time scale, so it is typically identified as complex. Complex network theory has been successfully applied in a variety of complex systems such as social networks, biological networks, and recently in climate networks.

In order to construct the complex networks from the El Niño datasets, we consider each point of latitude and longitude for periods of 365 days to remove seasonality in larger time windows. Each of these data sets forms a time series that corresponds to a vertex of the network. To make the connection between the vertices, the correlation between all the time series was calculated using the Spearman or Pearson coe�cients. We consider the correlation matrix to establish a connection between a vertex (series) and its adjacent ones that have a correlation coe�cient greater than 99.5% of the absolute values of correlation.

The reported analyzes show that some measures have proved to be effective for characterizing of El Niño. The Transitivity, Mean Length of Global Paths, Eigenvector, Eccentricity and Coreness values are higher in the years in which El Niño occurs, while Modularity has lower values. The measures Average Length of Paths and Average Degree did not present relation with El Niño, nor Betweenness and Clustering. It is concluded that complex networks are e�cient tools for the analysis of enormous volumes of data collected by satellites, radars and sensors. In addition, research on climate change can help people to face climate extremes and minimize their impacts."


Lilian Berton and Matheus Augusto De Castro SantosInstitute of Science and Technology - Federal University of Sao Paulo, Sao José dos Campos, SP 12247-014, Brazil {lberton}@unifesp.br

lanet-2019

Los bosques de Polylepis pueden ser considerados hábitats insulares para una gran variedad de organismos. Desde esta perspectiva, pueden ser analizados como una red compleja, más o menos conectada por las especies compartidas. Las comunidades deben ser homogéneas entre bosques cercanos y esto debe traducirse en altos valores de conectancia y anidamiento. En este trabajo, se presenta un análisis preliminar de la red de bosques de Polylepis ubicados en la Sierra Nevada de Mérida (Venezuela) con base en la riqueza de especies de briofitos. El inventario brioflorístico de cuatro bosques ubicados en el macizo del Pico Bolívar arrojó un listado de 52 especies, mayormente musgos (40 spp.), distribuidas en 23 familias y 38 géneros. La presencia-ausencia de las especies en cada bosque fue registrada en una matriz de adyacencia para determinar la conectancia (C) y anidación (NODF) del sistema. La conectancia fue estimada como el porcentaje de presencias observadas con relación a una distribución hipotética uniforme de las especies en todos los bosques. La anidación fue calculada con el programa ANINHADO que también permitió desarrollar el análisis de modelos nulos para determinar la significancia de esta métrica. La conectancia fue igual a 62,5 % y la anidación a 55.33 lo que sugiere un nivel medio de conectividad y anidamiento. La anidación, sin embargo, no difirió de la registrada en 1000 matrices artificiales (p>0,05), es decir, que el sistema no puede ser considerado más o menos anidado de lo esperado por efecto del azar. Por lo tanto, hace falta aumentar el número de bosques estudiados para esclarecer la topología del sistema y explorar las causas próximas de estos patrones comunitarios.

Jesús Francisco Delgado1 and Yeni Celeste Barrios2Laboratorio de Sistemática de Plantas, Departamento de Biología de Organismos, Universidad Simón Bolívar, Caracas, Venezuela. [email protected] Instituto Jardín Botánico de Mérida, Facultad de Ciencias, Universidad de Los Andes, Mérida, Venezuela. [email protected]

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Jesús Francisco Delgado and Yeni Celeste Barrios21

Comunidades de briofitos en bosques de Polylepis sericea Wedd. de la Sierra Nevada de Mérida (Venezuela): una aproximación desde la perspectiva de redes complejas


lanet-2019

Relation of basin stability of perturbed power systems on network heterogeneity and tripping time

Connectomics already has traced the connections of many nervous structures, performing the interpretation of their function. However, if we analyzed with few mathematical tools, we would lose information. To generate new mathematical tools, it is necessary that models grasp better the complexity of the nervous system. This study has two goals: a) make a neuron-glia network model compatible with existing mathematics theories and b) propose a new tool for connectomics. To modeling, firstly, we have defined two sets: a set "X" which includes a finite number of neurons (N) and glial cells (G), and a set "S" which includes synapsis. The last set contains three sets: tripartite synapsis, glia-glia interaction, and one glia-one neuron interaction. Secondly, from the first type of connection, we have built a distance rule D which relates two neurons by the number of neurons that are in the shortest walk which connect both in the network. We have created a metric space defined as an ordered pair (N, D). Finally, we propose a way to find hidden patterns in the network using a shape matrix created from the distance rule. The nature of the model could be in essence a hypergraph being vertices neurons and glial cells, and hyperedges as the set of synapsis which could be multiple as in the case of tripartite synapsis. Also, the neural component could be a digraph for the direction of its connections, and the glial cell component could be a simple graph. On the other hand, this model can be a natural structure where the synapsis is the NE-connection and the distances rule, the distribution. In conclusion, we have built a model which consider the main kind of interactions present in the nervous system and facilitate the generation of mathematic constructs as distance or shape.

D Y N A M I C S

Arthur Montanari, Ercilio Moreira and Luis Aguirre

Graduate Program in Electrical Engineering of the Universidade Federal de Minas Gerais (UFMG),Minas Gerais, Brazil. [email protected]

lanet-2019


1. Klinshov, V.V., Kirillov, S., Kurths, J., Nekorkin, V.I.: Interval stability for complex systems. New Journal of Physics 20, 043040 (2018)

2. Menck, P.J., Heitzig, J., Kurths, J., Schellnhuber, H.J.: How dead ends undermine power grid stability. Nature Communications 5, 3969 (2014)

3. Nishikawa, T., Motter, A.E.: Comparative analysis of existing models for power-grid synchronization. New Journal of Physics 17, 15012 (2015)

4. Schultz, P., Heitzig, J., Kurths, J.: A random growth model for power grids and other spatially embedded infrastructure networks. European Physical Journal: Special Topics 223(12), 2593–2610 (2014)

lanet-2019

In this work, we consider a fully connected network with competition and collaboration between different sets of agents following a generalized Lotka-Volterra model. Based on a set of 5,000 simulations of a 10-agents network with symmetric “adjacency” matrices chosen at random, we applied a Bandt and Pompe analysis (ordinal patterns) to find out the pattern distributions, determine the Shannon Entropy (Hi) associated with it, compute the Disequilibrium (Di) (Euclidean distance) and, finally, obtain the statistical complexity (Ci) for each agent i of the system, i.e. Ci=DiHi.

We divided the results of a total set of 50,000 agents in three subsets according with their interaction profile Pi, which is defined as the number of competitive neighbors minus the number of cooperative ones for each agent i. More specifically, we defined three subgroups of agents: I) Mostly competitive interactions (Pi > +1), II) Quasi-neutral interactions (-1 <= Pi <= +1), and III) Mostly cooperative interactions (Pi < -1).

Show that, for subgroups I) and II), there are two clearly defined clusters at high and low complexities. More interestingly, for subgroup III), in addition to those clusters, a very low complexity cluster is found. In other words, when the interaction profile is nearly neutral or mostly competitive, the complexity is around Ci = 0.11 and Ci = 0.04. On the other hand, when an agent has most of its neighbors cooperating with it the final result is an extremely low complexity (Ci < 0.01), beside that of the other two clusters. This is compatible with our recently reported results in [1] where the main two clusters were identified. The contribution of the present work is to analyze the relation between complexity and interaction profiles of agents.

L.F. Caram, M. Ausloos, N. Almeira and C.F. Caiafa1 4;52;3 6

Laboratorio de Redes y Sistemas Móviles, FI-UBA, Ciudad Autónoma de Buenos Aires, ArgentinaSchool of Business, University of Leicester, University Road, Leicester, UK.Department of Statistics and Econometrics, Bucharest University of Economic Studies, Romania.Grupo de Teoría de la Materia Condensada, FaMAF, Universidad Nacional de Córdoba, Argentina.Instituto de Física Enrique Gaviola, CONICET, Argentina.Instituto Argentino de Radioastronomía. CCT La Plata, COINCET, Argentina.

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The effect of interaction profiles on the complexity in acompetitive-cooperative-mixed multiagent systeme

D Y N A M I C S


[1]LF. Caram, M. Ausloos, N. Almeira, CF. Caiafa, Medyfinol 2018 Proceedings, Santiago, Chile (2018). "

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Extracting multifractal properties from visibility networks

"Diverse methods have been extensively used to characterize the multifractality of complex systems, such as in the case of time series and complex networks. In many real time series, the presence of multifractal (MF) properties can be evaluated by extracting local scaling exponents, which characterize different mechanisms operating at different scales. Similarly, multifractal features can be obtained for network structures, in which a single scaling exponent is not enough

to characterize the heterogeneity of the complex structure. The concordance of multifractality between time series and complex networks is very important in order to explore transformations from time series to complex networks (and vice versa). In this work, we study the multifrac tality of well-known MF sequences when they are transformed into a network by means of the visibility method. In our approach, we introduce the weight in the links as the slope m_{ij} of each pair of nodes (i, j) to obtain a weighted network. Next, the adjacency-weighted matrix is used to construct the partition function and the generalized dimensions. We apply our procedure to the binomial-cascade model and the Log-normal W-cascades. Our results show that multi fractal properties observed in the original time series are also described by the behavior of the generalized dimensions obtained from the weighted visibility network."

D Y N A M I C S

Carlos Carrizales-Velazquez and Lev Guzman-Vargas

Unidad Interdisciplinaria en Ingeniería y Tecnologías Avanzadas, Instituto Politécnico Nacional, Ciudad de México, Mé[email protected], [email protected]

lanet-2019

The Stochastic Transport Dynamics of a Conserved Quantity on a Complex Network

The stochastic dynamics of conserved quantities is an emergent phenomenon observed in many complex systems, ranging from social and to biological networks. Using an extension of the Ehrenfest urn model on a complex network, over which a conserved quantity is transported in a random fashion, we study the dynamics of many elementary packets transported through the network by means of a master equation approach and compare with the mean field approximation and stochastic simulations. By use of the mean field theory, it is possible to compute an approximation to the ensemble average evolution of the number of packets in each node which, in the thermodynamic limit, agrees quite well with the results of the master equation. However, the master equation gives a more complete description of the stochastic system and provides a probabilistic view of the occupation number at each node. Of particular relevance is the standard deviation of the occupation number at each node, which is not uniform for a complex network. We analyze and compare different network topologies (small-world, scale-free, Erdos-Renyi, among others). Given the computational complexity of directly evaluating the asymptotic, or equilibrium, occupation number probability distribution, we propose a scaling relation with the number of packets in the network, that allows to construct the asymptotic probability distributions from the network with one packet. The approximation, which relies on the same matrix found in the mean field approach, becomes increasingly more accurate for a large number of packets.

D Y N A M I C S

Pablo Medina and Juan Alejandro Valdivia

Departamento de Física, Facultad de Ciencias, Universidad de Chile, Santiado, Chile [email protected]

lanet-2019

We study opinion formation in networks using an Ising model to include dialectic processes. In this model, a node has a higher confidence in its argument when it is formed in a heterogeneous context. In the case of Watts-Strogatz, Erdos-Rényi and Barabási-Albert networks, the changes in the most relevant statistical properties are presented, including those in the critical temperature of the phase transition, and the magnetization and energy curves. We propose that the social influence, as studied by the Sznajd model, is implemented in the degree of convincement of one node and, in an opinion formation process, it depends on the homogeneity or heterogeneity of its surroundings. This degree is measured as the Shannon entropy for the probability of having an adjacent node with same opinion, assuming that convincement is related with the occurrence of dialectic positions and arguments in the social milieu. By means of a simulation using the Metropolis method, the Ising model was implemented over the networks with the di�erence that when each node is randomly chosen, and then it changes its state, the interaction with their neighbors diminishes in an amount according to Shannons entropy, evaluated over the spin value of its nearest neighbors. Here the interaction is defined as the influence that the surroundings can excerpt over the node, therefore dialectic processes cause high nodal entropy. This model also presents a way of creating a weighted and directed network by introducing entropy into the interactions of spins, as the interaction constant or convincement of each node does not depend directly of the convincement of others and evolves over time in a manner similar as an Ising-PageRank model. It is also shown how an ordered system behaves, when there is a perturbation, in this case in the values of the interactions, while the results keep exhibiting the Classical Ising model phase transition, with the di�erence that the critical temperature changes. Finally, we present the evolution in the opinion formation process of some quantities defined in the model, as the nodal entropy and the asymmetry in the values of links.

Departamento de Física, Facultad de Ciencias, Universidad Nacional de Colombia, Bogotá, Colombia Departamento de Física, Facultad de Ciencias, Universidad de Chile, Santiago, Chile1

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Miguel Angel Quintero Martinez, Rafael German Hurtado Heredia and Juan Alejandro Valdivia2

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An Ising model of opinion formation in dialectic processes with opposing argumentsD Y N A M I C S

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In the context of phase sychronization in a small network of R\"ossler oscillators, we investigate what type of information can be gathered from measured time series by the {\it joint} application of multivariate singular spectrum analysis and the vector field phase approach. The results suggest that they are complementary, showing different aspects of the phenomenon.

Leonardo Luiz Portes, Leandro Freitas, Michael Small and Luis Aguirre1 2 3

Joint characterization of phase synchronization in networks with multivariate singular spectrum analysis and vector field phase

D Y N A M I C S

Complex Systems Group, Department of Mathematics and Statistics, University of Western Australia, Nedlands, Perth, Australia Instituto Federal de Educaçao, Ciência e Tecnologia de Minas Gerais, Campus Betim R. Itaguaçu 595, 32677-562 Betim, Minas Gerais, Brazil Departamento de Engenharia Eletrônica, Universidade Federal de Minas Gerais, Av. Antônio Carlos 6627, 31.270-901 Belo Horizonte, MG, Brazil

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We present PowerNet as a set of computational tools designed for the study of the structural-electrical properties influencing power system robustness against cascading failures. Figure 1 presents the organization of the toolbox in three different categories: Network-based models, connectivity properties and cascading failures attack. The network-based category includes a set functions able to map a power system described in a MATPOWER case file format to a graph stvructure containing all the network information. The connectivity properties category consist of functions evaluating edge-connectivity [2], natural connectivity [3], and minimum cut sets between others. The third category, contains functions to simulate attacks strategies and cascading failure efects in power network. The functions work on MATLAB [1].Examples of connectivity analysis are presented for several IEEE power systems.

Universidad Manuela Beltrán, Bogotá Colombia Universidad Nacional de Colombia, Bogotá, Colombia The University of Sydney, Sydney, NSW 2006 Australia

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Claudia Caro Ruiz, Jin Ma, Andrés Pavas and Eduardo Mojica Nava211;3 2

PowerNet - A Toolbox for Network-based Analysis of Power SystemsD Y N A M I C S


1. Zimmerman, R. D., Murillo-Sanchez, C. E., and Thomas, R. J. MATPOWER: Steady-State Operations, Planning, and Analysis Tools for Power Systems Research and Education. IEEE Transactions on Power Systems, vol. 26, no. 1, pp. 12-19, Feb. 2011.

2. Caro-Ruiz, C., Ma, J., Hill, D. J., Pavas, A., and Mojica-Nava, E., : Qualifying Line Significance on Cascading Failures using Cut-sets. 13th IEEE Milan PowerTech 2019, Milan, Italia

3. Jun, W., Barahona, M., Yue-Jin, T., Hong-Zhong, D.: Natural Connectivity of Complex Networks. IOP Publishing, Chinese Physics Letters vol. 27, no. 7, pp. 078902, Jul. 2010.

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In this work, the nonlinear stability of the Colombian power grid is studied through the well-known second-order Kuramoto model. Monte Carlo simulations are performed in order to estimate the basin stability and recovery time of each node in the complex network. This allow us to identify the most susceptible nodes to lose synchronization and cause network malfunction after random large perturbations.

Universidad Nacional de Colombia — Sede Manizales. Facultad de Ingeniería y Arquitectura. Departamento de Ingeniería Eléctrica, Electrónica y Computación, Percepción y Control Inteligente, Bloque Q, Campus La Nubia. Manizales 170003, Colombia {ccgalindog,gaosoriol}@unal.edu.coInstituto de Matemáticas Aplicadas, Grupo de Modelado Computacional / Dinámica y Complejidad de Sistemas. Universidad de Cartagena, Cartagena de Indias, 130001, Colombia [email protected]

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Cristian Galindo, David Angulo-Garcia and Gustavo Adolfo Osorio Londoño21 1

Dynamical stability analysis of the Colombian Power GridD Y N A M I C S

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Online Social Networks (OSN) have become the new channel of information, with the diffusion of rumors and news in Google+, Facebook, and Twitter. Several models have been proposed for analyzing the diffusion process. However, most models consider homogeneous probabilities and characteristics of the subjects.

The results indicate that propagation probabilities correlated to PageRank improve in at least 20% the outbreak in comparison to the random case (Tab.1). On the other hand, the K-core centrality has a little effect. In a controlling scenario, we reduced in a half the propagation probabilities of 0.5% of the highest PageRank nodes in the Google+ network. As results, the global propagation was reduced by 60%. It was necessary to reduce in a half the propagation probability of 70% random nodes for reaching a similar result.

Besides, we also studied the social curiosity phenomenon, which is an external factor that defines how likely a node will seek for information. We modeled the curiosity proportional to the fraction of informed neighbors over time. Results in the Google+ and Facebook real-world networks show that including the social curiosity, in a regime close to the epidemic threshold, increased the global propagation in 70% and 25% respectively. However, the higher the propagation probabilities, the lesser the curiosity impact. Additionally, the more homogeneous and planar the network, the more significant the curiosity impact.

We propose different models for exploring real scenarios in our study. We consider that each has its capacity to convince or to be convinced over time. Moreover, external factors can change the interest of the users, like social curiosity for instance. We are interested in predicting the users' potential of spreading and finding strategies for increasing the user/network propagation. For example, the propagation capacity of nodes could be related to their location (centrality) on the network (Fig. 1). In this sense, we analyzed the implications between heterogeneous transmissibility and the correlation to topological properties of the nodes. First, the propagation probabilities of nodes are randomly distributed as a power-law, keeping constant the mean in all simulations. Then, we attribute the probabilities to each node according to some centralities.

Didier Vega-Oliveros, Liang Zhao and Francisco Rodrigues1 2 3

Dynamical Control of the Diffusion Process on Social NetworksD Y N A M I C S

Department of Computing and Mathematics - University of Sao Paulo, Ribeirao Preto, SP 14040-901, Brazil School of Informatics, Computing and Engineering - Indiana University, Bloomington, IN 47408, USA Department of Applied Mathematics and Statistics, Institute of Mathematical and Computer Sciences, University of Sao Paulo, Sao Carlos, SP 13566-590, Brazil {davo,francisco}@icmc.usp.br

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In the context of Regional Innovations Systems, it is necessary to understand how the knowledge flows from universities to regions, because the differences of knowledge concentration explain the multiplicity of regional development levels and innovation capabilities between regions. We are taking into account the knowledge transfer (KT) via human capital, bachelors and graduate people, educated at universities, who move from universities to regions, and transmit their knowledge to regional organizations (Galán-Muros & Plewa, 2016; Hamm et al., 2012; Jaeger & Kopper, 2014; Lau & Lo, 2015; Miguelez & Moreno, 2015; Zygmunt, 2014).

This work is focused on mathematical model to explain the KT from universities to regional productive organizations as an epidemic process, where the contagion mechanism is the human contact. To perform the model, we analyzed seven Colombian regions and studied how human capital produced by university nodes has been absorbed by productive organization nodes. Furthermore, we consider the proportion of knowledgeable people absorbed by each region to determine the structure of the complex network (Cowan & Jonard, 2004; Melo, Beck, Pena, & Pare, 2018; Pare, Liu, Beck, Kirwan, & Basar, 2018).

The mathematical model is based on the epidemic process susceptible-infected-susceptible (SIS)(Kermack & McKendrick, 1927; Nowzari, Preciado, & Pappas, 2016). The spread parameters are related with the knowledge absorption capacity and the loss of knowledge rate in each node, these parameters are analogous to the infection and healing rate respectively. The model explains a positive relationship between the level of knowledge concentration; given by the proportion of Science, Technology, Engineering and Math (STEM) bachelors and graduates in a specific region; and the capacity of innovation given by the quantity of innovation products like patents, utility models and industrial licenses. The model is validated using the data of 13 consecutive years to show the evolution of the complex network.

PhD Student Universidad Nacional de Colombia, Bogotá, [email protected] Associated Professor Universidad Nacional de Colombia, Bogotá, [email protected]

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Arturo Melo and Jose Ismael Peña2 2

The Knowledge Transfer from Universities to Regions as an Epidemic ProcessE C O N O M I C & K N O W L E D G E

R E F E R E N C E S :Cowan, R., & Jonard, N. (2004). Network structure and the diffusion of knowledge. Journal of Economic Dynamics and Control, 28(8). https://doi.org/10.1016/j.jedc.2003.04.002Galán-Muros, V., & Plewa, C. (2016). What drives and inhibits university-business cooperation in Europe? A comprehensive assessement. R&D Management, 46(2), 369–382.Hamm, R., Kopper, J., Jäger, A., Karl, H., Strotebeck, F., & Warnecke, C. (2012). RegTrans – Zwischenbericht I Regionale Transfereffekte verschiedener Hochschultypen Analyse und Strategien für eine verbesserte Potenzialausschöpfung.Jaeger, A., & Kopper, J. (2014). Third mission potential in higher education: measuring the regional focus of different types of HEIs. Jahrbuch Fur Regionalwissenschaft, 95–118.Kermack, W. O., & McKendrick, A. G. (1927). A Contribution to the Mathematical Theory of Epidemics. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 115(772), 700–721. https://doi.org/10.1098/rspa.1927.0118 Lau, A. K. W., & Lo, W. (2015). Technological Forecasting & Social Change Regional innovation system , absorptive capacity and innovation performance : An empirical study. Technological Forecasting & Social Change, 92, 99–114.Melo, A., Beck, C. L., Pena, J. I., & Pare, P. E. (2018). Knowledge transfer from universities to regions as a network spreading process. 4th IEEE International Symposium on Systems Engineering, ISSE 2018 - Proceedings. https://doi.org/10.1109/SysEng.2018.8544398Miguelez, E., & Moreno, R. (2015). Knowledge flows and the absorptive capacity of regions. Research Policy, 44(4), 833–848.Nowzari, C., Preciado, V. M., & Pappas, G. J. (2016). Analysis and control of epidemics. IEEE Control Systems Magazine, 63(february), 26–46. https://doi.org/10.1109/MCS.2015.2495000Pare, P. E., Liu, J., Beck, C. L., Kirwan, B. E., & Basar, T. (2018). Discrete Time Virus Spread Processes: Analysis, Identification, and Validation. Conditionally Accepted to IEEE Transactions on Control Systems Technology: System Identification and Control in Biomedical Applications, 1–13.Zygmunt, J. (2014). Determinants of Knowledge Transfer Processes in a Region. Barometr Regionalny. Analizy i Prognozy, 12(2), 7–12."

lanet-2019E C O N O M I C & K N O W L E D G E

The growth of the Internet has allowed access to information in large data from various sources, has also favored the interaction of people through multiple technologies creating new social dynamics of communication. Digital social networks are one of these technologies used to relate in the digital world, enabling people to have social interactions that are explicit in texts, images, audios that can be analyzed to understand these new links of social relationship. Companies today use these new technologies to establish relationships with their customers and thus make their products or services known, thanks to the potential they offer to identify interest groups and expectations that are explicit in the links that exist between users and the texts they share where you express their "private states" or opinions, feelings or valuations of these companies and their products. Although the banking sector is one of the main sectors of the economy, it has been one of the most private and prudent to obtain information from digital social networks. However, in recent years they have strengthened their relationships with customers through these new technologies to offer their services, market segmentation, retain customers and understand their new needs. Therefore, this study performs a social network analysis (SNA) and computational linguistics by opinion mining (OM) and community detection (CD) on post text and structural links on Twitter about the banking sector in Colombia. This approach is made by detecting communities using Louvain's algorithm and scoring their results with the detection of topics made with Latent Dirichlet Allocation (LDA) to determine which groups share opinions and which is their polarity. Finally, structural relations were considered through the analysis of hashtags and mentions which are grouped through their texts and polarities, linking the feeling and thinking that are common in their users named "collective subjectivity".

Luis Gabriel Moreno Sandoval, Liliana Pantoja, Alexandra Pomares, Luis Orozco and Ricardo Acero

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Pontificia Universidad Javeriana, ColombiaUniversidad Nacional de Colombia, ColombiaUniversidad Externado, ColombiaPolitecnico Grancolombiano, Colombia

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Opinion mining in the Colombian banking sector through community detection and topic modeling in the social network Twitter

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Cowan, R., & Jonard, N. (2004). Network structure and the diffusion of knowledge. Journal of Economic Dynamics and Control, 28(8). https://doi.org/10.1016/j.jedc.2003.04.002Galán-Muros, V., & Plewa, C. (2016). What drives and inhibits university-business cooperation in Europe? A comprehensive assessement. R&D Management, 46(2), 369–382.Hamm, R., Kopper, J., Jäger, A., Karl, H., Strotebeck, F., & Warnecke, C. (2012). RegTrans – Zwischenbericht I Regionale Transfereffekte verschiedener Hochschultypen Analyse und Strategien für eine verbesserte Potenzialausschöpfung.Jaeger, A., & Kopper, J. (2014). Third mission potential in higher education: measuring the regional focus of different types of HEIs. Jahrbuch Fur Regionalwissenschaft, 95–118.Kermack, W. O., & McKendrick, A. G. (1927). A Contribution to the Mathematical Theory of Epidemics. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 115(772), 700–721. https://doi.org/10.1098/rspa.1927.0118 Lau, A. K. W., & Lo, W. (2015). Technological Forecasting & Social Change Regional innovation system , absorptive capacity and innovation performance : An empirical study. Technological Forecasting & Social Change, 92, 99–114.Melo, A., Beck, C. L., Pena, J. I., & Pare, P. E. (2018). Knowledge transfer from universities to regions as a network spreading process. 4th IEEE International Symposium on Systems Engineering, ISSE 2018 - Proceedings. https://doi.org/10.1109/SysEng.2018.8544398Miguelez, E., & Moreno, R. (2015). Knowledge flows and the absorptive capacity of regions. Research Policy, 44(4), 833–848.Nowzari, C., Preciado, V. M., & Pappas, G. J. (2016). Analysis and control of epidemics. IEEE Control Systems Magazine, 63(february), 26–46. https://doi.org/10.1109/MCS.2015.2495000Pare, P. E., Liu, J., Beck, C. L., Kirwan, B. E., & Basar, T. (2018). Discrete Time Virus Spread Processes: Analysis, Identification, and Validation. Conditionally Accepted to IEEE Transactions on Control Systems Technology: System Identification and Control in Biomedical Applications, 1–13.Zygmunt, J. (2014). Determinants of Knowledge Transfer Processes in a Region. Barometr Regionalny. Analizy i Prognozy, 12(2), 7–12."

In last years, many researches have been interesting in collaborative relationships between project teams in a company and the actors across the supply chain. Relationships management is hard work due to the globalization and the revolutionary advance of information and communication technologies (ICTs)[1]. The opportunity to work together despite the distance have allowed the aggrupation of co-workers from different organizations who interact in Global Projects[2]. Supply Chain Global Projects (SCGP) are di�cult to manage due to the complexity integration of actors with demographic, cultural, and professional differences and limit face to face interaction[3,4]. The aim of this study is identify the influence of the factors to contribute forming a collaboration network structure in SCGP. To do that, we applied the Collaboration Characterization Project Management model[6] and Network Analysis. Particularly, visual and descriptive analysis and Exponential Random Graph Model[7–13]were used. Research data was collected from emails of some real projects (large-scale, standard-scale, and small projects) developed by a company that provide intra-logistics warehouse automation solutions. To analysis the dynamics on collaborative relationships, we build temporal networks. We used the five phases of the Project Management Book of knowledge as time windows[14]. Findings reveal the more important roles and the changes on the structure of SCGP depends of the project phases. Additionally, modelling results indicate an association between the actors matching on exogenous attributes (Organizational structure, cultural diversity and frequency of contact) and greater-than-chance probabilities of project team roles to exhibit collaborative behaviours. These results evidence that the structure, evolution, and node match of SCGP depend of the project size. This study provides a first approach to understand how networking and collaborative relationships occurs between actors in network communities. Additionally, this study could be used to support the formulation of strategies to successfully manage collaborative relationship in SCGP.

Facultad de Ingeniería, Universidad de Ibagué, Ibagué, Colombia. Montanuniversität Leoben, Chair of Industrial Logistics, Leoben, Austria1

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Carlos Meisel, Jose Meisel, Helga Bermeo-Andrade, Laura Carranza and Helmut Zsifkovits1 11 2 1

Structure and evolution of collaborative relationships in supply chain global projectsE C O N O M I C & K N O W L E D G E

R E F E R E N C E S :1. Bala R, Krishnan V, Zhu W. Distributed Development and Product Line Decisions. Prod Oper Manag. 2014;23: 1057–1066. doi:10.1111/poms.12185 2. Binder J. Global Project Management: Communication, Collaboration and Management across Borders. Strateg Dir. 2009;25: 2–20. doi:10.1108/sd.2009.05625iae.001 3. Powell A, Piccoli G, Ives B. Virtual Teams: A Review of Current Literature and Directions for Future Research. SIGMIS Database. 2004;35: 6–36. doi:10.1145/968464.968467 4. Anantatmula V, Thomas M. Managing Global Projects: A Structured Approach for Better Performance. Proj Manag J. 2010;41: 60–72. doi:10.1002/pmj.20168 5. Mossolly M. Global Projects: A Conceptual Review on Execution Attitude in Multinational Corporations. Procedia - Soc Behav Sci. 2015;194: 125–133. doi:10.1016/j.sbspro.2015.06.125 6. Meisel CA. Collaborative Relationships in Supply Chain Management: A Case of Project Management Social Network Analysis. Doctoral Thesis, Montanuniversitaet Leoben. 2016. 7. Lusher D, Koskinen J, Robins G. Exponential Random Graph Models for Social Networks: Theory, Methods, and Applications. Cambridge University Press; 2013. 8. Hunter DR, Goodreau SM, Handcock MS. Goodness of Fit of Social Network Models. J Am Stat Assoc. 2008;103: 248–258. doi:10.1198/016214507000000446 9. Cranmer SJ, Desmarais BA. Inferential Network Analysis with Exponential Random Graph Models. Polit Anal. 2011;19: 66–86. doi:10.1093/pan/mpq037 10. Hunter DR, Handcock MS, Butts CT, Goodreau SM, Morris M. ergm: A Package to Fit, Simulate and Diagnose Exponential-Family Models for Networks. J Stat Softw. 2008;24: nihpa54860. 11. Robins G, Pattison P, Kalish Y, Lusher D. An introduction to exponential random graph (p*) models for social networks. Soc Netw. 2007;29: 173–191. doi:10.1016/j.socnet.2006.08.002 12. Snijders TAB, Pattison PE, Robins GL, Handcock MS. New Specifications for Exponential Random Graph Models. Sociol Methodol. 2006;36: 99–153. doi:10.1111/j.1467-9531.2006.00176.x 13. Robins G, Snijders T, Wang P, Handcock M, Pattison P. Recent developments in exponential random graph (p*) models for social networks. Soc Netw. 2007;29: 192–215. doi:10.1016/j.socnet.2006.08.003 14. Project Management Institute, editor. A guide to the project management body of knowledge (PMBOK guide). Fifth edition. Newtown Square, Pennsylvania: Project Management Institute, Inc; 2013.

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Labour networks, where industries are connected based on worker transitions, uncover the structure of knowledge diffusion and labour mobility in an economy. A particular instantiation of such a network is constructed for South Africa using a new administrative dataset, containing worker flows of approximately 80% of the formal economy between 2008-2017. The research presented in this paper is threefold. First the skill-relatedness network is constructed according the methodology of Neffke et al., where nodes represent industries and edges represent skill-overlap estimated using inter-industry worker transitions. The German skill-relatedness network is used as a benchmark against which the temporal and regional topological stability of the network is compared. The second part of the paper includes the partitioning of the South African skill-relatedness network into clusters using the Markov stability algorithm of Lambiotte et al. which is particularly well suited to finding clusters of substantially different sizes. This sheds light on the modular structure of the economy and reveals industries that exhibit high internal mobility and skill-overlap. Recently it has been suggested that the modular structure of skill-relatedness networks constrain mobility and knowledge diffusion, thereby limiting labour pools for firms and job pools for workers (O'Clery,2018). Inter-industry mobility is desirable as it enhances firm learning and innovation, employment resilience and adaptability. Finally, the skill-relatedness network is used in order to investigate gender inequalities within the labour market. Entrance paths into the formal workforce for each gender are examined and potential gender-based entry barriers identified.

Samuel Heroy

The network dynamics of urban agglomerationE C O N O M I C & K N O W L E D G E

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The analysis of the different productive sub-sectors, disaggregating sectors up to 4 digits based on the SCIAN-2018, for a specific region like a State, a city or a region, using attributes such as annual personal average salary, total employees per subsector, number and sizes of companies, or amount of total production per subsector, allows to understand the impact to the Economy. The study on these attributes can also help to identify vulnerabilities, robustness and resilience among sectors and their interactions which could reinforce the economic dynamic in a specific sector, as well as to find new opportunity areas. In the present work, applying complex network approach, we analyzed these properties of the Mexi-can State of Aguascalientes by constructing for the productive sector a set of functional weighted networks linked to the corresponding sub-sectors being the edges weights the value of the attribute, generating with this arrangement a multi-layer structure. The results of network metrics are key concepts for decision-makers to develop economic policies.

Facultad de Ingeniería, Universidad Nacional Autónoma de México [email protected], [email protected], [email protected], [email protected]

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Javier Lara de Paz , Idalia Flores de la Mota , Gabriel Policroniades Chipuli Sashiko Shirai Reyna

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Multi-Layer Weighted Network for Aguascalientes SCIAN 4-digits Economic SectorsE C O N O M I C & K N O W L E D G E

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Crime in Latin American cities is one of the main concerns in terms of policy and decision making. 44 out of the top 50 most dangerous cities in the world are in Latin America and it has impacts for citizens, governments and institutions. Crime and fear of crime are one of the reasons why people move, why we observe more social segregation and fear of crime is one of the main concerns of individuals in the region.

Yet, in terms of fear of crime, it has been detected that many more individuals are fearful than victimised and there is a mismatch between the levels of fear and the levels of crime on a city. Many experiences show that a city can properly manage its crime, reducing and controlling its victimisation rates, but many cities have been unable to do anything with respect to their perception. A linear relationship between crime and fear of crime is often assumed, but results are often counterintuitive and show that some cities might be perceived as more dangerous even with less crime.

Building on a network-based model which considers fear of crime as a shared opinion between individuals, a model which takes into account segregation, victimisation and memory will be presented.

Results of the model help explain why more individuals are fearful than victimised and help us understand the long-term impacts of crime. Fear will be a persistent perception of Latin America for the upcoming decades, even if crime is properly managed."

Rafael Prieto Curiel

Modelling fear of crime in citiesI N T E R D I S C I P L I N A R Y

Department of Mathematics, University College London, Gower Street,LondonWC1E6BT,UK

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Music is an abstract process rooted in human nature that conveys sensations and sophisticated emotions. Graph theory has been used to recognize and characterize constitutive elements in music, to explore dynamical properties of sets of musical pieces, as well as to produce pieces with characteristics that resemble musical genres or styles. Here we study musical pieces departing from the tonal consonance properties of musical intervals in terms of physical parameters, mainly the pitch of musical notes. The model relates pitch and tonal consonance with the musical concepts of size and position in the register of musical intervals. First, we study melodic lines by representing their time evolution as directed graphs, with nodes assigned to pitches and links to musical intervals. We show that the weight of links can be modeled using an exponential probability distribution associated to their tonal consonance, given by the frequency of the pitches of both nodes, with information about the selection of intervals made by the composer, including the asymmetry in the use of ascending and descending intervals. Introducing an entropy extremalization principle we find conserved quantities with musical meaning, described in terms of physical parameters, with information about the use of tonal consonance. We also construct a dual representation of melodic lines, where melodic intervals are both nodes and links, and present some relations between topological properties in both representations. In this new representation the degree distribution introduces new information about melodic lines, with conserved quantities associated to the selection of successions of melodic intervals made by the composer.

Jorge Useche and Rafael Hurtadol

Networks of melody: The complex use of tonal consonance in musicI N T E R D I S C I P L I N A R Y

Department of Physics, Universidad Nacional de Colombia

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Estructura silábica de los 'Cien sonetos de amor' de Pablo NerudaI N T E R D I S C I P L I N A R Y

Este trabajo se desarrolló con la finalidad de observar en los sonetos, particularmente en los 'Cien sonetos de amor' de Pablo Neruda, la selección y uso de las sílabas así como patrones de orden en distintos niveles. Estos niveles son verso, estrofa, soneto y el compendio de los 100 sonetos como un todo.

El motivo para estudiar la composición y uso de sílabas en poesía es el interés por indagar sobre aspectos sonoros de estas obras, que dan contenido adiconal al mensaje textual que presentan. La razón para escoger los sonetos dentro de las diversas posibilidades que da la literatura es su regularidad, aunque ésta varía según la época, gracias a la cual podemos análizar algunos aspectos de de su estructura. Se tomó a Neruda y sus 'Cien sonetos de amor' por la figura del autor, que permiten encontrar información sobre la obra para contrastar los resultados obtenidos, y la estructura de los escritos. El corpus está compuesto por sonetos sin una rima completamente definida y sin todos ser endecasílabos. Estas dos características nos permiten hacer un análisis de categorización e indagación interesante. El método de análisis utilizado toma como base de la métrica a la sílaba. Se aplican dos métodos de separación silábica, uno corresponde a la identificación silábica normal del castellano mientras que el otro es el análisis métrico usado propiamente en poesía. Para ambos métodos se generan dos relaciones entre las sílabas, una de sucesión entre los elementos y la otra de co-ocurrencia, puede ser en versos, estrofas o poemas, que nos permiten indagar por propiedades de escalamiento a nivel de estructura. Finalmente, hacemos un contraste entre los resultados obtenidos por ambos métodos con el fin de observar cómo la métrica poética en un escrito fija su estructura. Entre los principales resultados se encuentran las propiedades estadísticas de medidas de topología como el grado nodal, el peso de los vínculos y el coeficiente de clusterización, para los cuales se obtienen distribuciones de probabilidad que permiten establecer algunos aspectos sobre cómo están construidas las obras, y en particular sobre su heterogeneidad.

Universidad Nacional de Colombia, Bogotá D.C, Colombia [email protected] , [email protected]

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Daniel Ortiz, Rafael Hurtado1;2 1;3

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Centralidad y poder en los mundiales de Fútbol: un ejercicio didáctico

La Fédération Internationale de Football Association (FIFA) es la institución encargada de gobernar las distintas federaciones de fútbol y de establecer el conjunto de reglas de juego en el sistema, que rigen a los diferentes actores pertenecientes a los clubes y selecciones nacionales, así como los torneos. Dichos actores toman decisiones con el propósito de ganar y para ello acopian recursos y establecen estrategias, en particular los directivos y los técnicos procuran reunir un plantel de jugadores y diseñar las estrategias que aprovechen al máximo las características de cada jugador y sus posibilidades de interacción entre ellos. Las estrategias posibles dependen de la disponibilidad de jugadores en cada club o selección, sus roles de juego y las posiciones en las que actúan, junto con habilidades específicas de cada jugador. Por lo tanto, las decisiones de los directivos en la contratación de jugadores determinan en gran medida los resultados obtenidos, haciendo importante la vinculación de los jugadores más prestigiosos sea a los clubes que a las selecciones nacionales.

El propósito de esta presentación es didáctico, en particular aquí presentamos un ejercicio que aplica medidas de centralidad utilizadas con frecuencia en el Análisis de Redes (grado, intermediación, beta, Katz, PageRank, hubs y autoridades) y que pueden ser abordadas en el contexto de los campeonatos mundiales de fútbol, asociando estas medidas con las estructuras y dinámicas de prestigio e influencia. El modelo relaciona los clubes y a las selecciones según los jugadores que comparten para construir una red de dos modos, y luego por co-ocurrencia contrior redes de un modo para los clubes y las selecciones. Este análisis no utiliza directamente, las opiniones de los expertos o el público en general con respecto al prestigio de los jugadores o los equipos; ni información de los clubes sobre los valores económicos de cada integrante de los planteles en término del valor de cada jugador; tampoco utiliza los resultados obtenidos anteriormente por los clubes, técnicos o selecciones. Entre los principales resultados se encuentran las estructuras de prestigio y de preferencia, los cuales son comparados con los resultados obtenidos por las selecciones en los campeonatos mundiales."

I N T E R D I S C I P L I N A R Y

Edwin Tocora, Mario Angarita and Rafael Hurtado1 2 3

Universidad Panamericana, Guadalajara, México [email protected] Universidad Nacional de Costa Rica, Heredia, Costa Rica [email protected] Universidad Nacional de Colombia, Bogotá D.C, Colombia [email protected]

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The aim of this paper is to design an information system for managing the complexity of research in universities, through the measurement of its variety. We combined different complex systems models to measure and track the value chain of research at Universidad de Los Andes under the Viable Systems methodology proposed by Stafford Beer. First, we named the research system actors and processes, then we modeled the research value chain (primary processes) using System Dynamics. Second, we proposed 28 indicators for measuring the value chain of research in three dimensions stated at the main university goal: funding, alignment, and visibility (Information processes). Third, we built a collaboration network with researchers as nodes and relationships of work as edges to understand communication mechanisms (Organizational processes). Finally, we evaluated the attachment behavior of the network and the normalized mutual information (NMI) among the community structure of the network and the value of each indicator. Our results showed that the collaboration network follows a preferential attachment behavior, where most of the collaborations are concentrated in a few researchers. We found that the community structure of the collaboration the network is related to the values of accumulated production, consistent authority, cooperation with all researchers inside the university, school name, department name, and years producing. Our results will inform the research o�ce and coordinators to make informed decisions based on data for future interventions, furthermore, our methodology for modeling research could be replicable to different institutions. To our knowledge, this is the first work that designs a model for measuring research under a complex systems approach and relates the designed model with collaboration networks using network science.

Felipe Montes, Ana Maria Jaramillo, David Hernando Bello and Silvia Restrepo1 1 22

Department of Industrial Engineering, Universidad de los Andes, Social and Health Complexity Center, Bogotá, Colombia. [email protected] Vice-presidency of Research, Universidad de los Andes, Bogotá, Colombia.

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Designing an Information System for Measuring and Tracking Research: a Complex Systems Approach

I N T E R D I S C I P L I N A R Y

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"In the study of economic and social networks there is a strong interest in analyzing strategic interactions and games on networks; (Bramoullé et al., 2014) establish games of strategic substitutes and use elements of spectral graph theory to characterize the sets of equilibria of these games. Their main results indicate that the stability of the equilibrium depends on the characteristics of a non-directed network and the degree of substitutability (D) of the actions of the agents in the network. The network is characterized using the lowest eigenvalue (LE) of the adjacency matrix.

The authors formulate a relationship between LE and possible number of equilibriums of a network game and regarding the D parameter. At the same time they find that the LE is higher in magnitude when the network topology is bipartite.

Another strand of the networks literature is concerned with developing simple network formation models. An important question at the juncture of these two strands of the literature entails understanding the distribution of minimum eigenvalues of the adjacency matrix delivered by different well established network formation processes, and thus the properties of the equilibria of games of strategic substitutes played on networks generated by these different processes.

To address this question, we focus on the network formation framework proposed by Jackson et al., (2007), given that it is capable of generating a broad range of networks of importance in many applications, including preferential attachment (PA) networks, and Ërdos-Renyi(ER) networks.

We simulate network formation and we find that a network formed through ER process has a LE with a lower magnitude than a network whose mechanism of formation is PA. Then a graph with PA model formation tends to have a bipartite topology structure, while a graph that is formed starting of random process, shows a less bipartite structure.

Maykol Rodríguez-Prieto, Carlos Castro-Irragori and Tomás Rodríguez Barraquer1 1 2

Universidad del Rosario, ColombiaUniversidad de Los Andes, Colombia"

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Network Formation and Equilibrium OutcomesM O D E L S


Bramoullé, Y., Kranton, R, and D’Amours, M. (2014): “Strategic interaction and networks”, American Economic Review 2014, 104(3): 898–930.

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A class of probabilistic multilayer networksM O D E L S

Enrique Hernandez-Lemus, Guillermo de Anda-Jáuregui and Jesus Espinal-Enriquez

National Institute of Genomic Medicina, México

A wide variety of complex phenomena in the physical, biological and socio-political sciences has become amenable to study, largely due to the progressive abundance

of larger, more comprehensive databases. This Big Data revolution, has brought the need to develop more powerful analytic approaches and computational techniques that will enable us to understand at a deeper level the intricate relationships that lie within such large data corpora. Complex networks in particular have been extremely successful to provide insight in the structure and function of natural, technological and social systems.

However, the full potential of the complex network approach has not been exploited yet. Two main avenues of improvement of the network paradigm have gained interest recently. First, the recognition of the multidimensional nature of many complex systems, conformed by a multitude of descriptive levels or layers, has led to the development of multilayer network theory. Multilayer networks constitute a solid and powerful approach to the study of complex phenomena. Second, the actual hierarchical structure of many complex systems can only be accessed via data generated in high-throughput experiments or empirical observations, that by necessity carry on their own set of biases, noise and other sources of complexity, the development of probabilistic approaches to network inference from large, noisy datasets is also an area of increasing interest. Here we introduce probabilistic weighted and unweighted multilayer networks as derived from information theoretical correlation measures on large multidimensional datasets. We will present the fundamentals of the formal application of probabilistic inference on problems embedded in multilayered environments, as well as a couple of examples taken from the analysis of biological and socio-political systems."

lanet-2019

Self-organization via consecutive preferential attachment episodesM O D E L S

Alberto Robledo

Instituto de Física and Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, Mexico

We explain how self-organization takes place in a statistical-mechanical model [1] designed to have such capability via a sequence of differentiations. The self-organizing system undergoes symmetry breaking phase transitions leading to a marginally stable multi-partitioned state. The family of transitions corresponds to a progression of preferential attachment networks or Yule’s processes characterized by power-law degree distributions with cubic decay. The entire development is described by a thermodynamic potential that transforms itself between differentiations. For each partition stage the correlation length first increases as the relevant thermodynamic field decreases, diverges at the symmetry breaking transition leading to phase separation, and then decreases as the field continues to decrease. When the correlation length vanishes the degrees of freedom that led to differentiation become frozen and the thermodynamic potential changes form and represents a new possible Yule process for which the degrees of freedom are capable of a further kind of differentiation. By construction [1] the model is analogous to the period doubling, or similarly, the chaotic-band splitting, cascades of attractors in dissipative nonlinear iterated maps.

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[1] A Robledo, Self-organization and a constrained thermal system analogue of the onset of chaos, EPL, 123 (2018) 40003. Doi: 10.1209/0295-5075/123/40003 "

lanet-2019

Lung cancer is the most deadly type of cancer worldwide. The mutation rate, copy number alterations, differentially expressed genes and epigenomic regulation are consistently more frequent and intense than in other cancers. These factors strongly suggest that the global process of transcription is profoundly altered.

To understand at the genome-wide level the alteration of transcriptional regulation, it is necessary the comprehension of normal (healthy) transcriptional process, and how this process varies during cancer.

An integrative manner to achieve the aforementioned goal is the reconstruction of the transcriptional program in health and disease by means of network science. Here we present the Whole-Transcriptome Regulatory Networks (WTRN), which are correlation networks of RNA transcripts inferred from hundreds of whole-transcriptome lung cancer samples, as well as from healthy adjacent lung tissue. In this representation, networks are composed by nodes that correspond to genes, and links are the non-linear correlation among the gene expression of them.

The WTRN presented here correspond to lung adenocarcinoma (n=502 lung cancer samples), squamous lung cancer (n= 533), as well as their respective healthy counterpart (n=49, and n=59). We compared the network structure between healthy and cancer networks, and also the similarity between normal lung network structures and between lung cancer networks.

The main difference between healthy and cancer networks, is that the strongest correlations in cancer occur between physically close genes (in the same chromosome), meanwhile the healthy networks present correlations between genes from different chromosomes. Furthermore, adenocarcinoma and squamous lung cancer networks are very similar in their structure; meanwhile their healthy counterpart networks are quite different between them. We argue that loss of heterogeneity is highly associated to physical/mechanic aspects that occur inside the nucleus of the cancer cell. Understanding this novel effect will have relevant implications in basic and clinical research."

Jesus Espinal-Enriquez, Sergio Daniel Andonegui-Elguera and Enrique Hernandez-Lemus1 21

National Institute of Genomic Medicine, México. http://www.inmegen.gob.mx INMEGEN, México. http://csbig.inmegen.gob.mx"1

2

Loss of heterogeneity in lung cancer networksN E T W O R K M E D I C I N E & H E A L T H

lanet-2019

Lung cancer is one of the most significant causes of death in the world. Lung cancer cell undergo genomic instability events that alter gene expressions. We propose a network analysis approach to reach a description of whole-transcriptome regulation in lung cancer.

We inferred whole-transcriptome regulatory networks (WTRN) starting from lung cancer RNAseq files. Adenocarcinoma (Ad) and squamous (Sq) lung cancer are the most common lung cancer subtypes. To build four networks, we used 502 Ad samples, and 533 Sq samples separately, as well as 49 and 59 of healthy adjacent tissue respectively. Mutual information was calculated with ARACNE algorithm. Whole-Networks were compared between them, and important genes in cancer were analyzed.

Lung cancer networks showed minor similarity with the healthy counterpart, nevertheless, high similarity between cancer networks. The healthy networks are comprised by a giant component, while lung cancer networks by numerous small components, generally genes from the same chromosome. The intersection (nodes and links shared) between healthy networks showed scarce similarity. Meanwhile, cancer networks shows large similarity. An intersection between Ad or Sq with their healthy counterpart shows almost nothing shared genes.

12p and 14q13 amplifications, has been frequently reported on Ad lung cancer with RAS and SFTA3 affected. Genes related to RAS and SFTA3 are different in Ad network and healthy counterpart. On the other hand, EGFR-AS1 expression has been associated to a single nucleotide polymorphism on EGFR, in Sq lung cancer. Sq lung cancer network exhibit a strong correlation between EGFR and EGFR-AS1, whilst in healthy network EGFR is associated with GOLGA8A and QPRT.

Our networks capture with a high accuracy several events of the transcriptional regulatory program. This approach might be relevant and useful for basic and clinical research to depict and understand transcriptional regulation in lung cancer"

Sergio Andonegui, Jesus Espinal-Enriquez and Enrique Hernandez-Lemus1;2 1;2 1;2

Computational Genomics Division, National Institute of Genomic Medicine, Mexico City, Mexico. National Autonomous University of Mexico, Mexico City, Mexico1

2

Whole-Transcriptome Regulatory Networks of Lung CancerN E T W O R K M E D I C I N E & H E A L T H

lanet-2019

Network Science has developed new biomarkers and models to detect and predict symptoms appearance and evolution. In general, network parameters and their alterations have been used to distinguish patients from control groups in different clinical studies (i.e, during memory tasks, the outreach parameter, the participation coe�cient and even the overall functional connectivity (FC) can be used to identify Mild Cognitive Impairment (MCI). In recent years, the scope of the network approach has been broadened by the exploration of multiplex and multilayer networks, a general framework suitable to study systems with different types of connections between nodes, such as transportation networks, socioeconomical systems or the brain. Concerning functional frequency-based multilayer networks, different models have been proposed. In this contribution, we introduce the first results analyzing magnetoencephalographic frequency-based multilayer networks of 48 healthy subjects, 46 MCI, and 17 Alzheimers disease in resting state. Datasets are composed of 30 trials, with time series of 4000 samples each, recorded at 1000 Hz Fs and previously filtered between 2 and 45 Hz. Our analyses are conducted at the scalp sensors level, and their reconstruction at the “regions of interest” (ROIs) level, using the Harvard-Oxford atlas.

From each time series we estimate its frequency components (5 different bands): δ (1-4), θ (4-8), α (8-13), β (13-31) and γ (31-50) Hz, associated with different brain processes. We get the supra adjacency matrix for each subject using the mutual information as the weight of the links connecting each pair of time series (after thresholding via permutation test). We obtain the second smallest eigenvalue of their Laplacian matrix, associated to the network structure, as well as some network parameters for each frequency band: outreach, global and local e�ciency, weighted clustering coe�cient and strength. Finally, we link the alterations of the algebraic connectivity with the evolution of AD.

Ignacio Echegoyen, David Lopez, Fernando Maestú, and Javier Martín Buldú1;2 33 1;2

Laboratory of Biological Networks, Centre for Biomedical Technology (UPM), 28223, Pozuelo de Alarcón, Madrid, Spain Complex Systems Group & G.I.S.C., Universidad Rey Juan Carlos, 28933 Móstoles, Madrid, Spain Cognitive and Computational Neuroscience Laboratory, Center for Biomedical Technology-CTB (UPM-UCM) *[email protected]

1

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From healthy aging to Alzheimer’s disease: A multilayer network approachN E T W O R K M E D I C I N E & H E A L T H

lanet-2019

Lung cancer is one of the most significant causes of death in the world. Lung cancer cell undergo genomic instability events that alter gene expressions. We propose a network analysis approach to reach a description of whole-transcriptome regulation in lung cancer.

We inferred whole-transcriptome regulatory networks (WTRN) starting from lung cancer RNAseq files. Adenocarcinoma (Ad) and squamous (Sq) lung cancer are the most common lung cancer subtypes. To build four networks, we used 502 Ad samples, and 533 Sq samples separately, as well as 49 and 59 of healthy adjacent tissue respectively. Mutual information was calculated with ARACNE algorithm. Whole-Networks were compared between them, and important genes in cancer were analyzed.

Lung cancer networks showed minor similarity with the healthy counterpart, nevertheless, high similarity between cancer networks. The healthy networks are comprised by a giant component, while lung cancer networks by numerous small components, generally genes from the same chromosome. The intersection (nodes and links shared) between healthy networks showed scarce similarity. Meanwhile, cancer networks shows large similarity. An intersection between Ad or Sq with their healthy counterpart shows almost nothing shared genes.

12p and 14q13 amplifications, has been frequently reported on Ad lung cancer with RAS and SFTA3 affected. Genes related to RAS and SFTA3 are different in Ad network and healthy counterpart. On the other hand, EGFR-AS1 expression has been associated to a single nucleotide polymorphism on EGFR, in Sq lung cancer. Sq lung cancer network exhibit a strong correlation between EGFR and EGFR-AS1, whilst in healthy network EGFR is associated with GOLGA8A and QPRT.

Our networks capture with a high accuracy several events of the transcriptional regulatory program. This approach might be relevant and useful for basic and clinical research to depict and understand transcriptional regulation in lung cancer"

N E T W O R K M E D I C I N E & H E A L T HAnálisis de la configuración de los determinantes de la salud desde las Redes Bipartitas

Rafael Renteria-Ramos, Rafael Hurtado-Heredia and Piedad Urdinola

Universidad Nacional de Colombia, Bogotá D.C, Colombia

En el ámbito de la epidemiología y la salud pública, algunos aspectos relacionados con la adherencia específica de eventos de salud a un territorio deben incorporar factores sociales, económicos, demográficos y ambientales. A través de ellos se puede establecer la configuración de patrones en los desenlaces en salud de grupos poblacionales expuestos a los mismos determinantes de salud o factores de riesgo. En este trabajo se presenta una metodología para la identificación de patrones asociados a configuraciones de factores de riesgo y determinantes de morbilidad de la población.

El caso de estudio es la población clasificada por SISBEN en el nivel I y II y las víctimas del conflicto armado interno ubicadas en el Departamento de Risaralda en Colombia. A partir de un análisis de redes bipartitas, tomando medidas de topología y motifs de los pacientes del sistema de salud.

Dentro de los resultados obtenidos se destaca la similitud de factores determinantes en enfermedades cardiovasculares para ambos grupos poblacionales, pero con mayor intensidad de la privación de las víctimas del conflicto armado. En otras palabras, las víctimas tienen mayor afectación del bienestar que la población clasificada por el SISBEN en nivel I y II, ante la exposición a los mismos determinantes y factores."

lanet-2019

Network Science has developed new biomarkers and models to detect and predict symptoms appearance and evolution. In general, network parameters and their alterations have been used to distinguish patients from control groups in different clinical studies (i.e, during memory tasks, the outreach parameter, the participation coe�cient and even the overall functional connectivity (FC) can be used to identify Mild Cognitive Impairment (MCI). In recent years, the scope of the network approach has been broadened by the exploration of multiplex and multilayer networks, a general framework suitable to study systems with different types of connections between nodes, such as transportation networks, socioeconomical systems or the brain. Concerning functional frequency-based multilayer networks, different models have been proposed. In this contribution, we introduce the first results analyzing magnetoencephalographic frequency-based multilayer networks of 48 healthy subjects, 46 MCI, and 17 Alzheimers disease in resting state. Datasets are composed of 30 trials, with time series of 4000 samples each, recorded at 1000 Hz Fs and previously filtered between 2 and 45 Hz. Our analyses are conducted at the scalp sensors level, and their reconstruction at the “regions of interest” (ROIs) level, using the Harvard-Oxford atlas.

From each time series we estimate its frequency components (5 different bands): δ (1-4), θ (4-8), α (8-13), β (13-31) and γ (31-50) Hz, associated with different brain processes. We get the supra adjacency matrix for each subject using the mutual information as the weight of the links connecting each pair of time series (after thresholding via permutation test). We obtain the second smallest eigenvalue of their Laplacian matrix, associated to the network structure, as well as some network parameters for each frequency band: outreach, global and local e�ciency, weighted clustering coe�cient and strength. Finally, we link the alterations of the algebraic connectivity with the evolution of AD.

N E T W O R K M E D I C I N E & H E A L T HCaracterización del estado de salud de poblaciones a partir de propiedades estadísticas de patrones de morbilidad

Los patrones de morbilidad proveen información sobre el estado de salud de los individuos, un elemento relevante en procesos de diseño y ejecución de políticas en salud. El presente estudio describe una metodología para caracterizar topológicamente patrones de morbilidad de individuos y obtener propiedades estadísticas para las poblaciones, desde el enfoque de análisis de redes. Se construyó una representación relacional a partir de la co-ocurrencia de diagnósticos en las consultas médicas realizadas a cada paciente, la cual fue aplicada al Régimen de Salud Subsidiado de Risaralda-Colombia, para el periodo 2011-2015. Las propiedades estadísticas de los patrones evidencian distribuciones exponenciales para la probabilidad de que un paciente tenga en su trayectoria de morbilidad un cierto número de diagnósticos, y para el número de aristas se encuentra una Ley de Simon. En estas distribuciones se identificaron diferencias por género y edad. Finalmente, se probó la robustez de la metodología propuesta en cuanto a cambios en el tamaño de las poblaciones, lo que la hace una herramienta susceptible de ser implementada para aplicaciones prácticas.

Carolina Saavedra-Moreno, Rafael Hurtado, Rafael Renteria, Piedad Urdinola and Nubia Velasco

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Facultad de Ingeniería, Universidad de Ibagué, Ibagué, Colombia [email protected] Facultad de Ciencias, Universidad Nacional de Colombia, Bogotá, Colombia [email protected],[email protected],[email protected] Facultad de Administración, Universidad de los Andes, Bogotá, Colombia [email protected]

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N E T W O R K M E D I C I N E & H E A L T HUnique deregulation patterns in breast cancer metabolic networks

Erandi Serrano-Carbajal, Jesus Espinal-Enriquez and Enrique Hernandez-Lemus

National Institute of Genomic Medicine Mexico

Metabolic reprogramming is a relevant hallmark of cancer. Cancer cells modify their flux balances to elevate energy, increase cell growth and proliferation. However, cancer heterogeneity makes extremely hard to study those patterns.

Breast cancer is divided into four molecular subtypes. Each subtype have a particular gene expression profile, and they correlate with a particular prognosis, response to therapy and malignancy. Notwithstanding, metabolic deregulation has not been studied taking into account the molecular subtype. Given this heterogeneity, our objective is to evaluate at transccriptional level, the deregulation in breast cancer metabolic networks.

Having the hypothesis that each subtype will have its own metabolic deregulation pattern, in this work we used a single-sample-based method that quantifies the deregulation in all metabolic networks, ranking each sample according to its deregulation score. We analyzed data from 1200 samples of primary breast cancer tissue gene expression. As we expected, deregulation patterns in metabolic networks are different for each subtype. We found four different groups in the deregulation patterns. Those groups strongly correlate to characteristic molecular subtypes of breast cancer. A particularly relevant result was observed in lipid metabolism. This is the only metabolic network that is not deregulated in any subtype, suggesting that it is not affected by any type of breast cancer.

We conclude that the deregulation metabolic patterns could help to distinguish the heterogeneity of breast cancer. The absence of deregulation in lipid metabolism could be a promising field of research since it was the only network not affected by cancer. Since this approach quantify deregulation at single-sample level, it may help to drive research in the searching for a personalized medicine.

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N E T W O R K M E D I C I N E & H E A L T HLeveraging network and cross-impact balance (CIB) analyses to understand the influence of transportation and food systems on health in Latin America

Introduction: Cities are complex systems; they are heterogeneous, interconnected and adaptive. Understanding the interdependencies between people, their health, and facets of urban living is important to identifying health promoting conditions. Food and transport systems, which represent two important facets of urban life, were the focus of two Group Model Building workshops conducted in Peru and Brazil in 2018. The aim of this study was to enrich the findings of these workshops by elucidating the strength and nature of the interrelationships between aspects of the food and transport systems, and health, and to identify the most stable configurations of cities (consistent scenarios) within each region.

Methods: We recruited eleven food and transportation experts from academia, government and NGOs (Peru, n=6; Brazil, n=5). Each participant completed a researcher-guided questionnaire where they identified the strength and nature of all possible bivariate relationships (edges) between all 11 food and transportation factors, each with two possible states – high and low (nodes). Using CIB analysis [1], we identified those scenarios that were most consistent with the overall directed network and the set of interactions that uniquely underpinned these scenarios. Network analysis was then used to characterize the network of each region.

Ivana Stankov, José David Meisel, Felipe Montes, Andres Felipe Useche, Amélia Augusta de Lima Friche, Lídia Maria de O. Morais, Peter Hovmand, Brent A. Langellier, Olga L.

Sarmiento, Ross A. Hammond & Ana V. Diez Roux

1 2

5;6

3

3

3

4 4 5 1

1

Drexel University Universidad de Ibague Universidad de los Andes Universidade Federal de Minas Gerais Washington University in St. Louis The Brookings Institution

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456

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The CIB analysis uniquely identified consistent scenarios for each region, including six in Brazil and seven in Peru (Figure 1A-B). Overall, a higher proportion of health-promoting scenarios were identified in Brazil than Peru. The directed networks underpinning these scenarios (Figure 1C-D) include 20 out of 22 possible nodes and differ by region with respect to the strength, clustering coe�cient and number of edges (n=116 and n=82 for Peru and Brazil, respectively). While ‘political will for change’ was identified as a highly influential factor (highest out-degree centrality) in both regions, ‘high healthy food prices’ and ‘high car use’ were also influential in Brazil, whereas ‘low car’ use and ‘high physical activity’ were prominent in Peru.

Unique interrelationships exist between key aspects of the food and transport system and health in Brazil and Peru. Food pricing, physical activity and car use may represent important intervention points for promoting health and wellbeing in these two regions.

1. Weimer-Jehle, W., Cross-impact balances: A system-theoretical approach to cross-impact analysis. Technological Forecasting and Social Change, 2006. 73(4): 334-361

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lanet-2019N E T W O R K M E D I C I N E & H E A L T HDifferential identifiability improves connectome predictive modeling of cognitive deficits in Alzheimer’s Disease

Mapping functional brain biomarkers to cognition r is imperative to treating behavioral and cognitive disorders. Functional connectivity (FC) biomarkers, may be able to fill this gap, however, low FC reliability and lack of generalizability of predictive models hamper clinical utility. Here we unify the frameworks of differential identifiability (Idiff) [1] and connectome predictive modeling (CPM) [2], showing that maximizing identifiability improves performance of CPM of deficits in Alzheimer’s disease (AD).

Resting-state fMRI Scans and neurocognitive testing of 82 individuals from the Alz-heimer’s Disease Neuroimaging Initiative were used together. Processed fMRI time series were split into halves (“RestA” and “RestB”). Two FCs were estimated per subject. FCs were decomposed/reconstructed using group level PCA to find the maximum differential identifiability (Idiff). The effect of Idiff on CPM of neurocognitive outcomes was assessed using a leave-one-out paradigm. CPM was performed separately on RestA and RestB FCs, such that corre-sponding model instances were estimated using FCs from the same subjects, different sessions. The “test-retest” paradigm assessed how Idiff in FC affected “between-session” stability of CPM. The leave-one-out paradigm assessed how Idiff affected CPM generalizability to external data. (1) Edge selection was performed to create masks of significant edges for a given outcome measure, (2) model fitting was per-formed to estimate parameters predicting the outcome measure from FC, and (3) ac-curacy/generalizability of the model were evaluated. “Between-session” stability of edge selection was evaluated using mask overlap. “Between-session” stability of mod-el fitting was evaluated using the sum of squared errors (SSE) between coe�cients. Anatomical specificity of edge selection for specific cognitive deficits was evaluated by assessing pairwise mask overlap across outcome measures (Figure1B, outcome similarity). Model accuracy/generalizability was evaluated using models from Res-tA FCs to predict cognitive outcomes from RestB FCs.

Diana Svaldi, Joaquin Goni, Enrico Amico, Kausar Abbas, David Clark, Shannon Risacher, John West, Mario Dzemidzic, Andrew Saykin, and Liana Apostolova

1

1 1 1 1 1

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Indiana University School of Medicine, Indianapolis IN 46202, USA School of Engineering, Purdue University, Lafayette IN 47907, USA [email protected]

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M E T H O D S

I N T R O D U C T I O N

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Differential identifiability was optimal at 82 PCs (Fig.1A). Results show that the reconstruction based on maximizing Idiff also optimizes key properties related to CPM: mask stability (Figure1B), coe�cient stability (Figure1C), and generalizability to external FC data (Figure1D). Our framework integrating CPM and differential identifiability represents an important step in improving the clinical utility of FC biomarkers.

1. Amico, E. and J. Goni, The quest for identifiability in human functional connectomes. Sci Rep, 2018. 8(1): p. 8254. 2. Shen, X., et al., Using connectome-based predictive modeling to predict individual behavior from brain connectivity. Nat Protoc, 2017. 12(3): p. 506-518.


R E S U L T S A N D C O N C L U S I O N S

lanet-2019

N E T W O R K M E D I C I N E & H E A L T HTeoria de Redes Complexas como abordagem para análise da acessibilidade geográfica da oferta potencial de serviços de saúde brasileiros

Este trabalho propõe criar um modelo lógico e aplicado de representação das entidades pertencentes ao Cadastro Nacional de Estabelecimentos de Saúde (CNES) do Sistema Único de Saúde Brasileiro (SUS) baseado na Teoria de Redes Complexas com o objetivo de analisar a acessibilidade geográfica da oferta potencial de serviços públicos de saúde. O SUS apoia seus processos de gestão em um conjunto de sistemas de informação que dão apoio às áreas: epidemiológica, ambulatorial, hospitalar e administrativa. Tais sistemas têm abrangência nacional e são mantidos pelo Departamento de Informática do SUS (DATASUS). Cada um destes sistemas apresenta perspectivas próprias e fragmentadas, levando o processo de gestão a lidar com diferentes fontes de informação, nem sempre atendendo às demandas dos gestores em face da complexidade do setor e suas articulações intersetoriais.

Quando se observa o aspecto fragmentado dos sistemas de apoio à gestão do SUS à luz de seu imperativo de ser um sistema integrado de saúde pública percebem-se lacunas. Há necessidade de desenvolver novas ferramentas e técnicas de acesso e análise destes dados permitindo: (A) Uma visão de sua realidade complexa; (B) A capacidade de integrar fontes adicionais externas ao ecossistema do DATASUS; (C) A capacidade de analisar comportamento emergente oriundo do relacionamento de diferentes camadas de dados.

Tendo em vista a dificuldade de se realizar análises integradas orientadas a diferentes fontes de dados – sejam estas fontes participantes do ecossistema do DATASUS ou externas – este trabalho aplica a Teoria de Redes complexas como ótica de análise explorando a perspectiva de acesso geográfico aos serviços de saúde dentro da Região Metropolitana de Brasília e integrando os dados do CNES com os dados sociais e econômicos disponíveis no Instituto Brasileiro de Geografia e Estatística (IBGE) disponíveis através do CENSO de 2010.

Deivson Rayner Teixeira da Costa, Ricardo Barros Sampaio and Jorge Otávio Maia Barreto

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Universidade de Brasilia, Brazil Fundação Oswaldo Cruz, Brazil1

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R O B U S T N E S SAnalysis of the relationship between multifractality and robustness in complex networks

Carlos Andrés Delgado Saavedra and Victor Andres Bucheli Guerrero

Universidad del Valle, Cali, Colombia

Through the complex networks we can model real systems through graph-based representations. These networks can be characterized with multiple structural metrics such as grade distribution, grouping coe�cient, grouping coe�cient and others.

In other hand, the structural metrics have limitations in the structure of the internal components, because these model the network as a whole and not as a set of interconnected internal structures. The multifractal analysis allow to study the network structure and shape their macro structure. This analysis can be performed with the box counting algorithms.

However, the multifractal analysis is a general measurement of the structure of a network and does not allow to obtain information about the dynamics of their structure, so our proposal a and approach that consists in analyze the relationship between multifractality and robustness.

Our methodology consists involve designing and executing some experiments using generated scale-free, small-world, random and reported networks in the literature. These experiments consist of calculating the fractal dimensions each time the network loses a percentage of its nodes due to a disturbance and get the R index of the robustnes.

Our main results are that small-world networks tend to be a homogeneous structure, as has been reported in the literature due to their multifractal curves are linear regardless of the loss of nodes. Then, this implies that their structure is preserved although the network is affected by attacks. Likewise, in scale-free networks, when the network losses a few nodes, the multifractal analysis shows a curve that indicate the presence of different structures into the network, but when the network losses nodes due to their degree or centrality, the multifractality curve tends to be linear, these results point out that their structure tends to be homogeneous or compact.

lanet-2019

Este trabalho propõe criar um modelo lógico e aplicado de representação das entidades pertencentes ao Cadastro Nacional de Estabelecimentos de Saúde (CNES) do Sistema Único de Saúde Brasileiro (SUS) baseado na Teoria de Redes Complexas com o objetivo de analisar a acessibilidade geográfica da oferta potencial de serviços públicos de saúde. O SUS apoia seus processos de gestão em um conjunto de sistemas de informação que dão apoio às áreas: epidemiológica, ambulatorial, hospitalar e administrativa. Tais sistemas têm abrangência nacional e são mantidos pelo Departamento de Informática do SUS (DATASUS). Cada um destes sistemas apresenta perspectivas próprias e fragmentadas, levando o processo de gestão a lidar com diferentes fontes de informação, nem sempre atendendo às demandas dos gestores em face da complexidade do setor e suas articulações intersetoriais.

Quando se observa o aspecto fragmentado dos sistemas de apoio à gestão do SUS à luz de seu imperativo de ser um sistema integrado de saúde pública percebem-se lacunas. Há necessidade de desenvolver novas ferramentas e técnicas de acesso e análise destes dados permitindo: (A) Uma visão de sua realidade complexa; (B) A capacidade de integrar fontes adicionais externas ao ecossistema do DATASUS; (C) A capacidade de analisar comportamento emergente oriundo do relacionamento de diferentes camadas de dados.

Tendo em vista a dificuldade de se realizar análises integradas orientadas a diferentes fontes de dados – sejam estas fontes participantes do ecossistema do DATASUS ou externas – este trabalho aplica a Teoria de Redes complexas como ótica de análise explorando a perspectiva de acesso geográfico aos serviços de saúde dentro da Região Metropolitana de Brasília e integrando os dados do CNES com os dados sociais e econômicos disponíveis no Instituto Brasileiro de Geografia e Estatística (IBGE) disponíveis através do CENSO de 2010.

R O B U S T N E S SSystemic Performance Measures from Distributional Zeta-Function

A topic of special interest in network science is the improvement of robustness and global performance in order to respond adequately to external disturbances. These issues are crucial for the sustainability of large scale dynamical networks from engineering to biological infrastructures. Recently, a kind of spectral functions of Laplacian eigenvalues called Systemic Performance Measures (SPM) were proposed to investigate network synthesis problems, such as, the growing of linear consensus networks. In this work we propose the use of the Distributional Zeta-Function for construct a new set of SPM. The Distributional Zeta-Function emerges from the replica method in disordered field theory. The adoption of this object has shown interesting physical consequences that in the usual replica method are still unclarified, i.e., the connection between the spontaneous symmetry breaking mechanism and the structure of the replica space in the disordered model. We relate the topological issues of the network and the partition function present in the Distributional-zeta function by using methods where the Hamiltonian of the system is replaced by the network Laplacian matrix and other related methods. We expose the advantages of this new set of SPM in the network synthesis and we propose other methods for the use of the Distributional Zeta-Function to explore some issues such as, disorder, critical phenomena, finite-temperature and finite-size effects on networks. Relevance of the results are discussed.

C.D. Rodríguez-Camargo and E. Mojica-Nava1;2 2;3

Centro de Estudios Industriales y Logísticos para la productividad (CEIL, MD), Departamento de Ingeniería Industrial, Corporación Universitaria Minuto de Dios, Bogotá AA 111021, Colombia Programa de Investigación sobre Adquisicion y Análisis de Señales (PAAS-UN), Universidad Nacional de Colombia, Bogotá AA 055051, Colombia Departamento de Ingeniería Eléctrica y Electrónica, Facultad de Ingeniería, Universidad Nacional de Colombia, Bogotá AA 055051, Colombia

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R O B U S T N E S SStructural stability of interaction networks against negative external fields

José Fernando Mendes

University of Aveiro, Portugal

I this talk I will show how can we explore structural stability of weighted and unweighted networks of positively interacting agents against a negative external field. Show that the agents support the activity of each other to confront the negative field, which suppresses the activity of agents and can lead to a collapse of the whole network. The competition between the interactions and the field shape the structure of stable states of the system. In unweighted networks (uniform interactions) the stable states have the structure of k-cores of the interaction network. The interplay between the topology and the distribution of weights (heterogeneous interactions) impacts strongly the structural stability against a negative field, especially in the case of fat-tailed distributions of weights. We show that apart critical slowing down there is also a critical change in the system structure that precedes the network collapse. The change can serve as early warning of the critical transition. In order to characterize changes of network structure we develop a method based on statistical analysis of the k-core organization and so-called ‘corona’ clusters belonging to the k-cores.

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S O C I A L N E T W O R K SAnalysis of Weighted Networks of Friendship and Animosity in Classrooms Groups

We conducted friendship and animosity surveys in 40 classrooms groups in the Mexico City Metropolitan area with students of different ages and levels. In each group students were asked to rate and order some of their peers in their classroom in two lists. In the first list each student wrote their 5 best friends in order, starting from the best friend, and in the second list they wrote the 5 “worst acquaintances” or “enemies”, starting from the worst of them. In this way we constructed 40 networks of interlinked friendship and animosity within each group.

For each person we can sum the total ”arrows” or directed links of either friendship or animosity to get useful information. A more complex description is obtained by assigning values to each arrow depending of the position in the survey lists. Then a person can get, for instance, a high value as a friend in two ways. First, if he or she is chosen as an “average friend” by many peers, or else, he or she is considered to be a “very good friend” by just a few peers.

Some general features were found in almost all networks, such as strong differences between friendship and animosity in the way the links are distributed, since friendship networks are distributed similar to random networks, while animosity networks tend to be more concentrated on fewer persons. That is, animosity links are focused more on less members, which is an interesting fact. It is important to mention that here we analyze these results from the descriptive and statistical point of view, so further work is needed to understand the causes of the general difference in the asymmetrical distribution of friendship and animosity links in the surveyed groups.

Marcelo Del Castillo-Mussot and Eric Hernandez-Ramirez1 2

Instituto de Física, Universidad Nacional Autónoma de México (UNAM), México Instituto de Investigaciones Económicas Universidad Nacional Autónoma de México (UNAM), México1

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Barrios Pulido O. L., Ferged - Rivadeneira A., Jara D. Dulce M., Vélez C.

Quantil S.A.S., Colombia

Las redes sociales son un medio de transmisión de información entre conjuntos de usuarios que posibilitan el estudio de la interacción de los participantes y del flujo de las conversacinones mediante el uso de redes. Con el fin de entender el uso de estas herramientas digitales en la opinión de los colombianos sobre temas políticos y sociales se centra en el primer caso en la constitución del partido de las FARC (Fuerzas Armadas Revolucionarias de Colombia a Fuerza Alternativa Revolucionaria del Común) y la violencia de género. Para estudiar la interacción digital alrededor de estos dos temas se obtuvo inrformación de 42 semanas de Twitter y de comentarios a noticias en medios noticiosos web. Para el entendimiento se utilizan herramientas de machine learning para identificar el grado de agresividad y provación para cada mensaje. Además se hace uso de medidas propias de la topología de una red compleja como grado, centralidad de grado, fortaleza de enlace y densidad de grafo que presetan un acercamiento al comportamiento y opinión de los usuarios sobre las temáticas.

S O C I A L N E T W O R K SEstudio de difusión de información en temáticas de violencia en redes sociales

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We conducted friendship and animosity surveys in 40 classrooms groups in the Mexico City Metropolitan area with students of different ages and levels. In each group students were asked to rate and order some of their peers in their classroom in two lists. In the first list each student wrote their 5 best friends in order, starting from the best friend, and in the second list they wrote the 5 “worst acquaintances” or “enemies”, starting from the worst of them. In this way we constructed 40 networks of interlinked friendship and animosity within each group.

For each person we can sum the total ”arrows” or directed links of either friendship or animosity to get useful information. A more complex description is obtained by assigning values to each arrow depending of the position in the survey lists. Then a person can get, for instance, a high value as a friend in two ways. First, if he or she is chosen as an “average friend” by many peers, or else, he or she is considered to be a “very good friend” by just a few peers.

Some general features were found in almost all networks, such as strong differences between friendship and animosity in the way the links are distributed, since friendship networks are distributed similar to random networks, while animosity networks tend to be more concentrated on fewer persons. That is, animosity links are focused more on less members, which is an interesting fact. It is important to mention that here we analyze these results from the descriptive and statistical point of view, so further work is needed to understand the causes of the general difference in the asymmetrical distribution of friendship and animosity links in the surveyed groups.

S O C I A L N E T W O R K SCross-class networks in college: A Temporal social network analysis

Sociologists often emphasize segregation and inequality in terms of class. Yet, what happens when there are possibilities for inter-class contact in a society with great inequality and segregation? This paper is the first one studying the temporal social networks created around undergraduate students in a context of an unprecedented policy intervention that opened up the door to a massive number of the poorest students in Colombia to an elite university. The paper studies if the program is promoting inter-class friendship relations among students of low, middle and high socioeconomic classes. We collected social network data through online surveys at two times: at the second and fourth semester of these student’s careers. Using statistical social network analysis methods, we evaluate to what extent sharing a university institution and undergraduate degree program generates conditions to overcome the tendency to homophily, that is the tendency of people to establish relations with those they perceive to be most like themselves. Homophily can create within-group cohesion but at the same time can also lead to societal segregation or, in this case, inhibit integration. We conducted logistic regressions per layer to analyze the tie formation due to homophilic individual attributes and network structure metrics for each of four different undergraduate degree cohort cases. Our results suggest that at time 0 there is a tendency to form a friendship with people of a similar socioeconomic class. Although in average this tendency prevails after a year, there are some particular students who overcome that tendency. The characterization of those individuals will be a guide for policymakers in strategies for promoting interventions that promote the social interactions across social barriers to confront the high rates of inequality in Colombia and the Latin American region.

María José Álvarez, PhD, Ana María Jaramillo, M.Sc. ,Laura Fernanda Merchán ,Felipe Fajardo Vanegas ,Natalia Muñoz, Andrés Molano, PhD &

Felipe Montes, PhD

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Department of Sociology, Universidad de Los Andes, Bogotá, Colombia; Department of Industrial Engineering, Universidad de Los Andes, Bogotá, Colombia;Social and Health Complexity Center (SHCC), Bogotá, Colombia; Department of Economics, Universidad de Los Andes, Bogotá, Colombia; School of education, Universidad de Los Andes, Bogotá, Colombia

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S O C I A L N E T W O R K STemporal Social Network Analysis Of A Physical Activity Mobile Phone-based Intervention in Public Schools Of Bogotá, Colombia.

Childhood is a crucial period for developing healthy lifestyle habits, such as regular engagement in physical activity (PA). Active children have better physical, psychological and cognitive health indicators than insu�ciently active children. However, 80% of children worldwide are insu�ciently active. In Colombia, a middle-income country, PA and healthy behaviors interventions have shown to be cost-effective to face the spreading of unhealthy behaviors. The use of technology, such as text messages (SMS), has shown the potential to promote healthy lifestyles, cohesion and health-related structural changes through immediate feedback and advice. Nevertheless, there is not enough evidence for designing community-based interventions based on the use of information and communication technologies (ICT) to e�ciently target social network structures. The aim of this paper is to evaluate the effect of the program Modulo Activo Recreo Activo (MARA), a PA intervention, potentiated using ICT, in terms of social cohesion and health related behaviors using static and temporal social network approaches.

Felipe Montes, Valentina Cardozo, Ana María Jaramillo, Andrés Felipe Useche, Ana Maria Guerra, Catalina Obando, Jose D. Meisel, Ruth Hunter,

Silvia A. González , Olga L. Sarmiento

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5;6 6

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Department of Industrial Engineering, Universidad de los Andes, Cra 1 Nº 18A – 12, Bogotá, Colombia, [email protected]. INRIA, Institut du Cerveau et de la Moelle épinière, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, F-75013, Paris, France Faculty of Engineering, Universidad de Ibagué, Carrera 22 Calle 67 B/Ambalá, Ibagué, Colombia. School of Medicine, Dentistry and Biomedical Sciences, Queen’s University Belfast, University Rd, Belfast BT7 1NN, Belfast, United Kingdom Children’s Hospital of Eastern Ontario Research Institute. Healthy Active Living and Obesity Research Group. 401 Smyth Rd, Ottawa ON K1H 5B2, Ottawa, Canadá School of Medicine, Universidad de los Andes, Cra 7 # 116 – 05, Bogotá, Colombia

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Three schools were randomly selected in three intervention groups: MARA+SMS, MARA, and control. We collected friendship networks, PA, nutritional status, and anthropometrical measures in the baseline and a follow-up after 10 months. For each network, we calculated the Jaccard coe�cient to assess temporal social network changes over time. Then, we conducted a QAP logistic regression to understand the prevalence of friendship ties caused by homophilic attributes. Third, we calculated modularity to understand associations among segregation and (un)healthy behaviors. Fourth, a difference in difference test was conducted to evaluate the effect of MARA and SMS interventions. Finally, a Separable Temporal Exponential Random Graph Model was conducted to evaluate formation and dissolution of friendship ties due to MARA+SMS and MARA interventions. We found that the PA intervention with SMS promotes the cohesion of the network both in communities grouping and in the formation of new ties among children. Also, active children increased its friendship connections and were less likely to dissolve them. This community-based Intervention could modify health related behaviors among children, especially as a mechanism to change network structure.

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Jose Antonio Mendez-Bermudez

Instituto de Física, Benemérita Universidad Autónoma de Puebla, Apartado Postal J-48, Puebla 72570, Mexico [email protected] http://www.ifuap.buap.mx/ jmendezb/

Within a Random Matrix Theory (RMT) approach we have already studied topological [1], spectral [2–4], eigenvector [2,3], and transport [5] properties of ER–type random networks with a special focus on universality. Moreover, we have also performed scaling studies on other random network models, such as multilayer and multiplex networks [6, 7] and random–geometric and random–rectangular graphs [8]. Thus, following this trend, we present here eigenvector properties of a random bipartite graph model focusing on scaling and universality from a RMT point of view.

S T R U C T U R ERandom Matrix Theory approach to random bipartite graphs


1. Martinez-Martinez, C.T., Mendez-Bermudez, J.A., Rodriguez, J.M., Sigarreta J.M.: Scaling and inequalities on the Randic index in Erdo¨s-R´enyi models. Submitted (2019)

2. Mendez-Bermudez, J.A., Alcazar-Lopez, A., Martinez-Mendoza, A.J., Rodrigues, F.A., Peron, T.K.DM.: Universality in the spectral and eigenvector properties of random networks. Phys. Rev. E 91, 032122 (2015)

3. Martinez-Martinez, C.T., Mendez-Bermudez, J.A.: Information entropy of random networks with losses and gain: Scaling and universality. Entropy 21, 86 (2019)

4. Gera, R., Alonso, L., Crawford, B., House, J., Mendez-Bermudez, J.A., Knuth, T., Miller, R.: Identifying network structure similarity using spectral graph theory. Appl. Net. Sci. 3, 2 (2018)

5. Martinez-Mendoza, A.J., Alcazar-Lopez, A., Mendez-Bermudez, J.A.: Scattering and transport properties of tight-binding random networks. Phys. Rev. E 88, 122126 (2013) 6. Mendez-Bermudez, J.A., Ferraz-de-Arruda, G., Rodrigues, F.A., Moreno, Y.: Scaling properties of multilayer random networks. Phys. Rev. E 96, 012307 (2017)

7. Mendez-Bermudez, J.A., Ferraz-de-Arruda, G., Rodrigues, F.A., Moreno, Y.: Diluted banded random matrices: Scaling behavior of eigenvector and spectral properties. J. Phys. A: Math. Theor. 50, 495205 (2017) 8. Alonso, L., Mendez-Bermudez, J.A., Gonzalez-Melendrez, A., Moreno, Y.: Weighted random-geometric and random-rectangular graphs: Spectral and eigenvector properties of the adjacency matrix. J. Complex Networks 6, 753 (2018)

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S T R U C T U R EVRP Complex Network Analysis and Simulation

One of the main characteristics of real transportation problems is not only the complexity of designing their models; but also, in the characterization of the elements that make up the supply chain. A more detailed will grant un-doubtedly a greater knowledge of the system. However, the use of regular networks for the development of models can eliminate key characteristics for correct decision making. Faced with this fact, the characterization of transport models is proposed through the conceptualization of complex net-works. This new proposal will allow to apply metrics and perform more de-tailed analyzes to the distribution network; such as vulnerability analysis, el-ement grouping identification, among other metrics involving complex net-works. The objective of this research is to show the application of the meth-odology developed by the authors, where simulation - optimization is pro-posed as a solution technique.

Gabriel Policroniades Chípuli , Idalia Flores de la Mota , Javier Lara de Paz , Oliva Sashiko Sihirai Reyna

1[a]

1[D]

1[B] 1[C]

Universidad Nacional Autónoma de México [email protected], [email protected] [email protected], [email protected]

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In the traditional approach of physics, complex phenomena is studied by mean of simple idealized models that facilitate the comprehension and execution of controlled experiments.

But in the emerging information era, its important to develop complementary statistical methods with the capacity to handle the accidental structure of complex phenomena.

In this work we present the Boltzmannian MDL (BMDL), a formalization of the principle of MDL for the statistical modeling of complex data, inheriting many of the advantages of the RMDL while overcoming most of its drawbacks.

The BMDL benefits from the theoretical background of statistical mechanics for systems in thermodynamic equilibrium, to take advantage on the existence of phase transition to justify the use of appropriate approximations and to define an information theoretic criterion to address the statistical significance of model selections.

To illustrate the use and benefits of the BMDL, we apply the BMDL formalism to the challenging problem of community detection in complex networks, showing how it can be used to derive the multi-resolution Newman-Girvan modularity for weighted networks. Then, we correspondingly derive a BMDL based multi-resolution community detection method analogous to the message passing algorithms of Zhang et al. [PNAS 111, 51, 18144-18149 (2014)], which can improve detection performance at the expense of over-fitting.

Juan Ignacio Perotti, Claudio Juan Tessone, Aaron Clauset, and Guido Caldarelli7;8;9

1 3 4;5;6

IFEG-CONICET, Facultad de Matemática, Astronomía, Física y Computación, X5000HUA Córdoba, Argentina. [email protected], [email protected] Social Networks, Universität Zürich, Andreasstrasse 15, CH-8050 Zürich, Switzerland Department of Computer Science, University of Colorado, 430 UCB, Boulder, CO, 80309, USA BioFrontiers Institute, University of Colorado, 596 UCB, Boulder, CO, 80309, USA Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM, 87501, USA IMT School for Advanced Studies Lucca, Piazza San Francesco 19, I-55100, Lucca, Italy 8Istituto dei Sistemi Complessi (CNR) UoS Sapienza, 00185 Rome, Italy European Centre for Living Technology, Universita` di Venezia “Ca’ Foscari”, 30124 Venice, Italy

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S T R U C T U R EMessage Passing Approximation of the Boltzmannian Minimum Description Length for Community Detection in Complex Networks

R E F E R E N C E S :[1] P. D. Gru¨nwald, The minimum description length principle. MIT press, 2007.

[2] J. I. Perotti, C. J. Tessone, A. Clauset, and G. Caldarelli, “Thermodynamics of the minimum description length on community detection,” arXiv preprint arXiv:1806.07005, 2018.

[3] S. Fortunato, “Community detection in graphs,” Phys. Rep., vol. 486, no. 3, pp. 75–174, 2010.

[4] J. Reichardt and S. Bornholdt, “Statistical mechanics of community detection,” Phys. Rev. E, vol. 74, no. 1, p. 016110, 2006.

[5] S. Fortunato and M. Barth´elemy, “Resolution limit in community detection,” Proc. Nat. Acad. Sci., vol. 104, no. 1, pp. 36–41, 2007.

[6] A. Arenas, A. Fernandez, and S. Gomez, “Analysis of the structure of complex networks at di�erent resolution levels,” New J. Phys., vol. 10, no. 5, p. 053039, 2008.

[7] R. Lambiotte, “Multi-scale modularity in complex networks,” in 8th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, pp. 546–553, May 2010.

[8] M. Newman, “Equivalence between modularity optimization and maximum likelihood methods for community detection,” Phys. Rev. E, vol. 94, no. 5, p. 052315, 2016.

[9] P. Zhang and C. Moore, “Scalable detection of statistically significant communities and hierarchies, using message passing for modularity,” Proc. Natil. Acad. Sci. USA, vol. 111, no. 51, pp. 18144–18149, 2014.[10] C. Shi, Y. Liu, and P. Zhang, “Weighted community detection and data clustering using message passing,” Journal of Statistical Mechanics: Theory and Experiment, vol. 2018, no. 3, p. 033405, 2018.

[11] A. Ghasemian, H. Hosseinmardi, and A. Clauset, “Evaluating overfit and underfit in models of network community structure,” arXiv preprint arXiv:1802.10582, 2018.

[12] A. Lancichinetti, S. Fortunato, and F. Radicchi, “Benchmark graphs for testing community detection algorithms,” Phys. Rev. E, vol. 78, no. 4, p. 046110, 2008.

[13] N. X. Vinh, J. Epps, and J. Bailey, “Information theoretic measures for clusterings comparison: Variants, properties, normalization and correction for chance,” Journal of Machine Learning Research, vol. 11, no. Oct, pp. 2837–2854, 2010.

P O S T E R S

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Taste does not endure: The dynamics of ingredient pairings, Colombia, 1977-2017

We use network analysis to study the development of food pairings in Colombia between 1977 and 2017. The research relies on a new dataset, consisting of recipes from the publication Revista Carrusel, a widely read magazine that consistently published recipes throughout the period of study. This dataset was collected through in situ archival research and comprises around 6,000 recipes.

The analysis was conducted using two network analysis approaches to analyze the topology of the network: two-mode network (recipes-ingredients) and one mode network (ingredients). The results show both a cross sectional and dynamic analysis of the network of Colombian ingredients combinations. Furthermore, the study evidences a core of ingredients possibly characterizing Colombian tastes as it adapts to the changing nature of ingredient availability as some peripheral ingredients gain popularity for short periods of time, but the core is relatively unchanged.

The main contribution for the discussion of ingredient networks is the dynamic element. Existing research on ingredient pairing and food recipes points out to the underlying chemical compound mechanisms that govern ingredient pairings, nevertheless they assume that recipes do not change over time, possibly due to lack of data [1–8] . By using this new dataset I point out to the importance of taking dynamics into account as ingredient pairings do change over time, particularly during festive periods such as Halloween, Christmas, and New Years. This research builds on recent research that covers one year of analysis to point out to the dynamics of food pairings [9]. "

Juan Carlos Sanchez Herrera

New York University, New York NY 10003, USA [email protected]. https://github.com/juancsherrera

R E F E R E N C E S :1. Min, W.; Bao, B.K.; Mei, S.; Zhu, Y.; Rui, Y.; Jiang, S. You Are What You Eat: Exploring Rich Recipe Information for Cross-Region Food Analysis. IEEE Transactions on Multimedia 2017, PP, 11.

2. Sajadmanesh, S.; Jafarzadeh, S.; Ossia, S.A.; Rabiee, H.R.; Haddadi, H.; Mejova, Y.; Musolesi, M.; Cristofaro, E.D.; Stringhini, G. Kissing Cuisines: Exploring Worldwide Culinary Habits on the Web. In Proceedings of the Proceedings of the 26th International Conference on World Wide Web Companion; International World Wide Web Conferences Steering Committee: Republic and Canton of Geneva, Switzerland, 2017; pp. 10131021.

3. Ahn, Y.-Y.; Ahnert, S. The Flavor Network. Leonardo 2013, 46, 272273.

4. Ahn, Y.-Y.; Ahnert, S.E.; Bagrow, J.P.; Barabsi, A.-L. Flavor network and the principles of food pairing. Sci. Rep. 2011, 1.5. Jain, A.; K, R.N.; Bagler, G. Spices form the basis of food pairing in Indian cuisine. arXiv:1502.03815 [physics, q-bio] 2015.

6. Zhu, Y.-X.; Huang, J.; Zhang, Z.-K.; Zhang, Q.-M.; Zhou, T.; Ahn, Y.-Y. Geography and Similarity of Regional Cuisines in China. PLoS ONE 2013, 8, e79161.

7. Kikuchi, Y.; Kumano, M.; Kimura, M. Analyzing Dynamical Activities of Cooccurrence Patterns for Cooking Ingredients. In Proceedings of the 2017 IEEE International Conference on Data Mining Workshops (ICDMW); 2017; pp. 1724"

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‘Los mismos con las mismas’? Closeness and political-elite persistence within the Colombian presidential kin network between 1811 and 2018.

In this exploratory paper, using a novel genealogical data set for all Colombian presidents between 1811 and 2018, I answer two questions. Fist, is the Colombian presidential history one of ‘los mismos con las mismas’? I find that it is so because out of 106 presidents, 98 (92.5%) have direct or indirect kin ties to each other. Furthermore, a graphical approach to the closeness between conservatives and liberals suggest that the ideological differences within the political elite are not as marked as the partisan divide would. Second, is there long-term systematic evidence supporting the hypothesis that closeness among presidents is a mechanism for political elite persistence? The geodesic distance analysis for the presidential kin network – the one formed by all presidents and their connecting relatives – suggests that it is so. The (overestimated) mean of the geodesic distance from a president to the closest president is a seemingly short one (around 5 degrees). Some further research questions are formulated opening a promising research agenda.

Felipe Perilla

Business School, Universidad de los Andes, Bogotá, Colombia. [email protected]

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Empirical analysis of the European international railway network

Railway transportation of passengers is increasing its importance in many countries thanks to the extension of high speed infrastructures and its advantages over air transport. In Europe, it is a competitive transportation mode for short and medium distance trips. In this paper we analyse the structure of the European international railway network (EIRN), using the commonly used metrics to describe complex networks (degree distribution, clustering coe�cient, centrality, community structure, etc.; see Figures 1 & 2). Although some studies of this sort dealing with railway networks have been published dealing with characteristics of specific countries (for instance, Greece [1] considering socio-economic variables to understand the current geographic extension; Ukraine [2] studying centrality of stations to identify investment priorities;or India [3] describing the networks and discussing its disassortative nature) we are not aware of any pan-European analysis as done here. We consider the stations as nodes and the existence of a train directly travelling from two stations as a link in the network. The network has 412 nodes and 7,732 links. Similar to other railway studies [4], it has been found that that the EIRN is a scale-free network with small-world features. Given the competition posed by air transport, it makes sense, when analysing this railway network and computing distance-based metrics, to use a certain distance threshold as cut-off so that trips longer than that are considered unlikely. This feature is innovative and requires adapting the way some metrics are measured.

The results of this analysis are compared with those found for the Chinese system, the second world largest railway network [5]. With 3,915 nodes and 22,259 edges, [6] have studied the Chinese transportation system and described its characteristics. Comparison between both networks including clustering measures, shortest path length, distributions of degrees and strengths, and correlation between them, are presented as well.

Belarmino Adenso-Diaz, Laura Calzada and Santiago Garcia Carbajal

Engineering School, University of Oviedo at Gijón, Spain.

R E F E R E N C E S :[1] Tsiotas, D. (2017) “Links between network topology and socioeconomic framework of railway transport: Evidence from Greece”, J. of Engineering Science and Technology Review, 10, 3, 175-187.

[2] But’ko, T.; Prokhorchenko, A. (2013) “Investigation into Train Flow System on Ukraine’s Railways with Methods of Complex Network Analysis”, American J. of Industrial Engineering, 1, 3, 41-45.

[3] Sen, P.; Dasgupta, S.; Chatterjee, A.; Sreeram, P.A.; Mukherjee, G.; Manna, S. (2003) “Small-world properties of the Indian railway network”, Physical Review E, 036106.

[4] Seaton, K. A.; Hackett, L. M. (2004) “Stations, trains and small-world networks”, Physica A, 339, 635-644.

[5] Zhang, Z.; Li X.; Li H. (2015) “A quantitative approach for assessing the critical nodal and linear elements of a railway infrastructure”, International J. of Critical Infrastructure Protection, 8, 3-15.

[6] Li, W.; Cai, X. (2007) “Empirical analysis of a scale-free railway network in China”, Physica A, 382, 693-703.

[7] Blondel, V. D.; Guillaume, J.-L.; Lambiotte, R.; Lefebvre, E. (2008) “Fast unfolding of communities in large networks”. Journal of Statistical Mechanics: Theory and Experiment, P10008

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Modelado de la red compleja de actividades a través de la dinámica de sistemas para un proyecto de infraestructura de transporte vial.

Gran parte del pensamiento que domina la gestión de proyectos como se practica y se enseña hoy se basa todavía en teorías de control, desarrolladas en algunos casos desde los años de 1950. En entornos complejos, los problemas de gestión surgen de suponer que los resultados, anticipados al inicio del proyecto, pueden ser suficientemente determinados y luego entregarse como estaba previsto. Sin embargo, una vez que un proyecto alcanza un tamaño crítico, un calendario, un nivel de ambigüedad e interconexión, los enfoques basados en el control no funcionan. Los proyectos que abordan situaciones complejas, pueden describirse como sistemas adaptativos complejos, consistentes en múltiples componentes dinámicos interdependientes, múltiples proce-sos de retroalimentación y relaciones no lineales. Este trabajo propone una perspectiva evolutiva, pasando de la perspectiva clásica basada en el estándar internacional PMBOK® [1] a la visión de la complejidad, a través de una red compleja se simula la estructura dinámica del proyecto y sus trayectorias. Se toma como caso de estudio para la validación, la simulación de un proyecto de intervención vial en la zona centro occidente de Colombia, departamento de Caldas – Municipio de Palestina. Esta simulación se presenta a través de un conjunto de actividades representadas por variables de nivel, éstas se definen como el porcentaje de ejecución de la actividad y están en función de la tasa de ejecución y salida de la misma, en aplicación de la metodología para elaborar modelos dinámi-cos de redes CPM y PERT [2], a través de la siguiente fórmula: NEAik=NEAij + (TEAijk-TRAijk)*dt A través del proceso de simulación se determinaron resultados para variables de: control, exóge-nas, endógenas, independientes, de resultado y de pronóstico. Finalmente a través de la simula-ción se pudieron detectar posibles desfases futuros y por ende incidencias de estos en los costos y cumplimiento del proyecto.

Luz Stella Cardona

Universidad Nacional de Colombia, Manizales, Colombia [email protected]


1. PMI. A Guide to the Project Management Body of Knowledge (PMBOK R Guide). 5th edn. Project Management Institute, Newtown Square, Pennsylvania (2013)

2. 2. González, L., Kalenatic, D. y López, C. Metodología para elaborar modelos dinámicos de redes CPM y PERT. Working paper. Bogotá D.C., pp.1-17 (2006)"

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Algorithmic Complexity As a Better Measure In a Real Social Network

Measuring the complexity of an object is the key to classifying and identifying its nature. In a previous work it was possible to quantify, through Shannon entropy, the impact of a psychological intervention made by a group of psychologists in a social network of hospital workers. However, it is known that Shannon's entropy contains syntactic information and a way to remove such ambiguity is using Algorithmic Complexity. Unlike Shannon's entropy, Algorithmic Complexity is invariant under the description of the object. The results obtained coincide with the previous work although with the improvement that the information is now semantic information and can be of great help not only in this work but in other studies with the Biophysics where it would help to interpret better polynucleotide sequence for example.

Alejandro Puga, Jose Abraham Hernandez and Maria Leticia Perez

Universidad Autonoma de Zacatecas

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On functional observability, sensor allocation and dynamical networks

High-dimensionality issues prevent the application of classical methods from control theory to interconnected sets of dynamical systems, hereby addressed as "dynamical networks". A recurring goal in the literature is the search for an optimal set of driver or sensor nodes that renders the system controllable or observable. This goal, however, is not quite realistic. From an observability point-of-view, the state reconstruction of a high-dimensional network would require an observation of almost all nodes [5]- which is not always physically or economically feasible. A more practical goal would require the measurement of only a small set of nodes of interest. To this end, a concept known as "functional observability" defines if a given set of outputs is su�cient to guarantee the existence of a stable observer capable of estimating a linear function of the state vector [4]. Applications of functional observability to dynamical networks include: scenarios where only a limited set of unobserved nodes should be estimated, network fault detection, or even the identification of clusters of synchronized nodes. Hence, although a small set of sensor nodes might not be su�cient to render a system fully observable, it can su�ce for functional observability, depending on the choice of the functional. In this work, we present a greedy algorithm, adapted from [6], for an optimal sensor allocation given a functional of interest. Grounded on observability coe�cients [2], we define a coe�cient of functional observability to serve as a cost function for the optimization problem. The study is validated on a set of susceptible-infectious-recovered (SIR) models interconnected accordingly to a small-world network [3] (discussed in Fig. 1). Having established functional observability, a functional observer is designed to estimate the desired functional. The applications to state feedback control as well as the current limitations to the design of functional observers are discussed at last.

Arthur Montanari and Luis Aguirre

Graduate Program in Electrical Engineering of the Universidade Federal de Minas Gerais (UFMG), Avenida António Carlos 6627, 31270-901, Belo Horizonte, Minas Gerais, Brazil. [email protected]


1. Aguirre, L.A., Portes, L.L., Letellier, C.: Structural, Dynamical and Symbolic Observability: From Dynamical Systems to Networks. PLoS ONE 13(10), e0206180 (2018)

2. Aguirre, L.A.: Controllability and Observability of Linear Systems: Some Noninvariant Aspects. IEEE Transactions on Education 38(1), 33–39 (1995)

3. Bichara, D., Kang, Y., Castillo-Chavez, C., Horan, R., Perrings, C.: SIS and SIR Epidemic Models Under Virtual Dispersal, vol. 77. Springer US (2015)

4. Jennings, L.S., Fernando, T.L., Trinh, H.M.: Existence conditions for functional observability from an eigenspace perspective. IEEE Transactions on Automatic Control 56(12), 2957–2961 (2011)

5. Motter, A.E.: Networkcontrology. Chaos 25, 097621 (2015)

6. Summers, T.H., Cortesi, F.L., Lygeros, J.: On Submodularity and Controllability in Complex Dynamical Networks. IEEE Transactions on Control of Network Systems 3(1), 91–101 (2016)

lanet-2019

About the interaction between active substances and diseases

The process of diversification of the drug discovery has been studied from different perspectives, ranging from market or financial views to local drug-disease interaction. Here, we study a new set of data that will be useful to researchers and practitioners in order to evaluate the role of active pharmaceutical ingredients at different levels [4]. Our dataset was constructed from a variety of publicly available data sources on drugs, pharmaceutical ingredients, diseases; in addition to associated categorical groupings such as ATC and ICD systems [5]. Specifically, our dataset integrates information from two major international sources: PLM and Vademeum. The data was automatically extracted from the websites: PLM Medicaments, publishing company that has been dedicated to the development and publication of pharmaceutical dictionaries; and Vademecum.es, recognized pharmacological handbook which includes advertising of medicines, prescription pharmaceutical specialties addressed to health professionals [5, 6]. These datasets do not provide a full downloadable file neither for the chemical substance or disease pairs to describe drugs or compare their indications between drug makers and health professionals. When we compare both sources of information, we observe that there is a good concordance between the groups of medicines and diseases. In our approach, a bipartite network is considered as a represen tation of multiple connections between diseases and active ingredients. Particularly, a node denotes either an active ingredient or a disease and a link represents an interaction, i. e., a relationship given by the prescription of a drug for a disease. First, we present some basic properties of the bipartite network derived from the association between active substances and diseases. We find that both outgoing and incoming links are well described by a stretched exponential distribution. In addition, we discuss some results of the local interaction between different active ingredients given by the intermediation of diseases.

Irene Lopez-Rodriguez and Lev Guzman-Vargas

Unidad Profesional Interdisciplinaria en Ingeniería y Tecnologías Avanzadas, Instituto Politécnico Nacional, Ciudad de México. [email protected] [email protected]


1. Krantz, A. Diversification of the drug discovery process. Nature Communications 16, 1294 (1998).

2.Leaders. Getting medicines to market faster. The economist. https://www.economist.com/leaders/2018/03/24/gettingmedicines-to-market-faster (March, 2018).

3. Fraenkel, L., Petters, E. Patient responsibility for medical decision making and risky treatment options. Arthritis Rheum 61(12), 1674 1676 (2009).

4. Hirschler, B. Big pharma, big data: why drugmakers want your health records. Reuters. https://www.reuters.com/article/us-pharmaceuticals-data/big-pharma-big-data-why-drugmakerswant-your-health-records-idUSKCN1GD4MM (March, 2018).

5. World Health Organization Anatomical Therapeutic Chemical Classification System. https://www.whocc.no/ 2018 6. https://figshare.com/s/008146b619d95c74f844

lanet-2019

Abrupt percolation transition on random networks induced by attacks on high-betweenness nodes

A fundamental issue concerning networked systems is their robustness to the failure of its elements. Of special importance is the case of targeted attacks, where nodes are removed in decreasing order of importance. Node importance is typically identified using centrality measures, such as its degree or betweenness. Networks with a broad degree distribution are fragile against attacks on their hubs. On the other hand, homogeneous networks are expected to be more robust. There exist, nonetheless, exceptions to this rule; real systems such as power grids and road networks are very fragile against attacks, in spite of being homogeneous in terms of their degree. It has been argued that betweenness heterogeneity, rather than degree heterogeneity, is the cause of network vulnerability. Indeed, scale-free networks have long-tailed betweenness distribution, as it happens with the two homogeneous networks mentioned before. In this work, we perform a systematic analysis of the robustness of random homogeneous networks studying the percolation transition induced by a betweenness-based attack on their nodes. We found that, when a small fraction of nodes is removed, this attack has a very poor performance, approximating random deletion. Nevertheless, there exists a critical fraction after which the network undergoes an abrupt transition, suddenly becoming disconnected. Using finite-size scaling analysis, we study the nature of this transition, which differs from other known transitions, such as those induced by random removal and degree-based attacks. Similar abrupt transitions have been previously found in the context of explosive percolation models under different dynamic rules, and in cascading dynamics on interdependent networks. Awareness of these transitions is of importance in the prediction and understanding of catastrophic events such as blackouts, severe tra�c congestion, and desertification processes.

Nahuel Almeira, Orlando Vito Billoni and Juan Ignacio Perotti

Universidad Nacional de Córdoba, Instituto de Fisica Enrique Gaviola – CONICET

lanet-2019

RED DE INTERACCIONES PLANTA-POLINIZADOR EN UN BOSQUE INUNDABLE DE LA CUENCA DEL LAGO DE MARACAIBO (VENEZUELA)

Con la finalidad de dilucidar cómo están organizadas las interacciones planta-polinizador en una comunidad inundable se registraron los polinizadores de 22 especies de plantas durante dos años y medio. Una vez completada la matriz de interacciones, se calcularon los índices de especialización (H2), anidación (wNODF), modularidad (M) y C-score (número de unidades tipo tablero de ajedrez), y se determinó la significancia de cada índice mediante el análisis de modelos nulos. Para evaluar posibles procesos generadores, se construyeron cuatro matrices basadas en (1) la posibilidad de acoplamiento morfológico de las especies, (2) la coincidencia en el horario de antesis y de actividad de los animales, (3) la combinación de las matrices 1 y 2, y (4) el azar. El AIC (“Akaike's information criterion”) fue calculado para cada una de las matrices lo que permitió seleccionar el modelo más verosímil. La fauna polinizadora estuvo representada por 49 spp., principalmente abejas (62.06 %), seguidas de avispas (13.79 %), dípteros (10.34 %), esfíngidos (6.89 %) y aves (6.89%). El polinizador más común en cuanto al número de especies polinizadas (n= 14 spp.) y su abundancia en la red (27.72 % de las interacciones) fue la abeja introducida Apis mellifera. El resto de los polinizadores interactuaron con 1-5 especies de plantas y fueron poco frecuentes (0.07-8.29 % de las interacciones). La estructura de la red fue altamente especializada y próxima a una configuración de tipo tablero de ajedrez (p< 0,05). Entre los modelos evaluados, el primero fue el más cercano a la estructura de la red (p= 1) lo que indica que la disyunción de las interacciones puede ser explicada por las restricciones fenotípicas de las especies. Finalmente, los resultados sugieren que la alta especificidad de las interacciones puede afectar la robustez de la red, sobre todo frente a eventos estocásticos de pérdida de especies.

Yeni Celeste Barrios and Nelson Ramírez1 2

Instituto Jardín Botánico de Mérida, Facultad de Ciencias, Universidad de Los Andes, Mérida, Venezuela. [email protected] Instituto de Biología Experimental, Facultad de Ciencias, Universidad Central de Venezuela, Caracas, Venezuela.

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lanet-2019

Commodore micro-RNAs in breast cancer molecular subtypes: functional regulation by highly connected, non-redundant micro-RNAs.

Gene regulatory programmes are altererd in every cancer manifestation. These gene expression profile alterations reflect the heterogeneity found in the clinical manifestations of cancer. In the case of breast cancer, it is possible to associate gene expression patterns to specific clinical features; an example of these are the breast cancer molecular subtypes: luminal A, luminal B, basal, HER2-enriched.

The mechanisms behind regulatory program alterations in cancer are many. Currenlty, the role of non-coding RNA, particularly micro-RNA, is of great interest for the biomedical community. Like other aspects of gene regulation, these may be approached from a network science perspective.

Recently, our group developed the concept of “Commodore micro-RNA” (cdre-miR), micro-RNAs which have high degree and low redundancy in micro-RNA/gene co-expression bipartite networks. We identified a small set of such micro-RNAs in samples of non-classified breast cancer. Each of these micro-RNAs has a neighborhood of genes associated to particular biological functions, indicating a possible role of cdre-miRs in the control of biological functions.

In this work, we look to answer the following questions: whether each molecular subtype of breast cancer has a different set of cdre-miRs, and whether the functions associated to cdre-miRs in each subtype are different. For this, we use data from the Cancer Genome Atlas (TCGA) to generate micro-RNA/gene co-expression bipartite networks for each breast cancer molecular subtype. We analyze the topology of these bipartite networks and identify a set of cdre-miRs, as well as the set of associated biological functions, for each subtype. Our results allow for the identification specific functional alterations in breast cancer subtypes, and may provide information for further research in therapeutic applications of micro-RNAs.

Guillermo de Anda-Jáuregui, Jesus Espinal-Enriquez and Enrique Hernandez-Lemus

Universidad Nacional de Córdoba, Instituto de Fisica Enrique Gaviola – CONICET

lanet-2019

Statistics of Quality and Technical Standards Networks in Colombia

How is the relational structure that characterizes the network composed by quality or technical certifications and companies in Colombia? Does this bipartite graph show the evolution of the quality or technical certifications service? In order to answer these research questions, we explored, using Social Network Analysis, the historical database of records about certifications granted by the Colombian Technical Standards Institute (ICONTEC). This kind of network is composed by a total of 10267 certifications and 396 companies. The certifications were granted from 1971 to January 2019 in different families of standards, among which are ISO, ANSI, ASTM, IEC, NTC, UNE-EN. 78% of organizations have been certified on ISO 9001, 15.3% on OHSAS 18001 and 14.8% on ISO 14001. As part of the results, we find that the cumulative distribution of yearly granted certifications follows an “S” like curve. Further, the network exhibits a power law behavior. As a consequence, the sectors and certifications´ network showed few certifications with a high degree and many sectors with low degree. We also find a core-periphery structure with three main layers. The first layer is the core, it is composed by companies awarded in the management HSEQ system (Health, Safety Environment and Quality). The middle layer is shaped by some sectorial certifications and their respective companies. While the outer layer is conformed by companies and certifications with few links. Moreover, we constructed a network that is highly fragmented, since it presents sectorial subcomponents. Finally, we discuss several implications that our results may have in the ICONTEC´s decision making process and the importance of government as a promoter of quality or technical certifications.

Jorge Mejia, Gina Paola Prieto Pinzón, Martha Lucía Castro, Diana Asprilla and Mónica Vivas11 222

Universidad Central, ColombiaICONTEC

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lanet-2019

Agent-Environment Mutual Information in a Potential Game Context

In this work we use the Gaussian process regression (GPR) and the rate distortion theory (RDT) to define a potential function that controls the movement of a set of agents (mobile sensors) in a 2-D spatial field, through their euclidean distance and the neighbor-based inference of their environment. First, agents use GPR in order to infer the environment behavior based on their neighbor information. Then, agents decide to follow redundant cues according to the level of mutual information between their sensed measurements and the inferred environment. We show that in an invariant environment, the agent behavior is similar to the consensus-based result obtained in \cite{Marden2009}. On the other hand, in a variant environment, agents develop movement trajectories based not only in the euclidean distance, but also in the measurements level.

David Martínez and Eduardo Mojica-Nava

Universidad Nacional de Colombia [email protected] [email protected]


1. Marden, J., Arslan, G., Shamma, J.: Cooperative Control and Potential Games. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 39(6), 1393–1407 (dec 2009)

2. Mart´ınez, D.A., Mojica-Nava, E., Watson, K., Usla¨nder, T.: MULTI-AGENT LEARNING FRAMEWORK FOR ENVIRONMENT REDUNDANCY IDENTIFICATION FOR MOBILE SENSORS IN AN IOT CONTEXT. In: 3rd International Conference on Smart Data and Smart Cities. vol. XLI, pp. 33–41 (2018) 3. Rasmussen, C.E.: Gaussian processes for machine learning. MIT Press (2006)

lanet-2019

Red de interacciones planta-visitante floral en una comunidad de páramo altiandino (Parque Nacional Sierra Nevada, Mérida, Venezuela).

Las interacciones entre plantas y visitantes florales en la alta montaña tropical pueden ser muy vulnerables frente al cambio climático. Con la finalidad de dilucidar cómo se organiza esta red, se registró la fauna antófila de una comunidad de páramo altiandino, se construyó una matriz de adyacencia, basada en la frecuencia de cada interacción, y se calcularon los índices de especialización (H2), anidación (wNODF) y modularidad (M). La significancia de cada índice fue determinada mediante el análisis de modelos nulos. Para evaluar los procesos influyentes, se construyeron matrices de probabilidad de interacciones basadas en: (1) ajuste fenotípico, (2) coincidencia fenológica, (3) coincidencia espacial, la combinación de las matrices anteriores y por último el azar. El AIC ("Akaike's information criterion") fue calculado para cada una de las matrices lo que permitió seleccionar el modelo más verosímil. Se registraron 72 spp. de visitantes florales en 20 spp. de plantas durante nueve meses. La fauna antófila estuvo representada por Diptera (56 %), Hymenoptera (24 %), Coleoptera (11 %), Lepidoptera (3 %), Psocoptera (1 %) y Thysanoptera (1 %) de la clase Insecta, así como por Trochiliformes (3 %) y Passeriformes (1 %) de la clase Aves. Las familias más importantes de dípteros fueron Tachinidae, Muscidae y Cecidomyiidae. Las tasas de visitas a nivel general fueron bajas (<0,03 visitas/hora/flor). Los valores de anidación y modularidad no difirieron de las expectativas aleatorias (p>0.05), sin embargo, la red fue medianamente especializada (p< 0,05) lo que sugiere una robustez moderada del sistema frente a eventos estocásticos de pérdida de especies. El modelo más cercano a la red fue el basado en la combinación de las matrices 1 y 2 (p=1) lo que muestra una alta influencia de las restricciones fenotípicas y fenológicas en los patrones de interacción, este último aspecto particularmente vulnerable frente al cambio climático.

Oriana Manrique Pérez, Yeni Celeste Barrios, Néstor Sánchez and José Ramón Grande Allende

Universidad de los Andes, Venezuela

lanet-2019

Representación y análisis de los procesos de desintegración de las partículas subatómicas aplicando el formalismo del Análisis de Redes

El modelo que actualmente proporciona una de las mejores descripciones teóricas de los fenómenos relacionados con las partículas subatómicas en el Universo es el Modelo Estándar de la Física de Partículas, que incluye el modelo de Glashow-Weinberg-Salam y la Cromodinámica Cuántica [1,2]. Este extraordinario ingenio de la ciencia acomoda de manera elegante una amplia variedad de fenómenos asociados a de las interacciones fundamentales, aunque no es capaz de explicar cuanto se ha observado experimentalmente. Por este motivo, actualmente se busca en los experimentos de partículas, principalmente en el gran colisionador de hadrones del CERN, el LHCb, nuevas partículas o manifestaciones de las interacciones fundamentales que proporcionen pistas para nuevos modelos teóricos más apropiados. Siendo así, siguiendo perspectivas similares de otros trabajos [3,4,5,6,7] representamos los procesos de desintegración por medio de grafos para una red de información experimental disponible, incluyendo el Particle Data Group [8]. La red esta preparada como un grafo dirigido cuyos nodos son las partículas subatómicas y los vínculos indican el número de canales de desintegración que pueden llevar de una partícula a la otra. Con el fin de establecer algunas propiedades estadísticas de estos procesos, tomamos medidas de topología de este grafo para cada partícula [6], como su grado, o para diadas, como su peso.

Es así que para este análisis son presentados los valores de algunas propiedades topológicas asociadas a partículas que tienen papeles especiales en el mundo subatómico, así como propiedades del conjunto de todos los decaimientos [1,9]. Se muestra que existe un patrón en la relación entre la masa de las partículas y su grado, pero que esta relación no es trivial, también se establecen patrones de estructura asociados a la equivalencia regular entre varias partículas que cumplen funciones importantes en el sistema, mostrando la capacidad de esta representación para extraer información grupal como también local.

Camilo Naizaque, Rafael Hurtado, Diego Milanes and Cesar Daniel Rondon Duarte1 1;21 1

Universidad Nacional de Colombia, Bogotá D.C, Colombia. CERN Esplanade des Particules 1 Geneva, Switzerland.

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1. David J Gri�ths. Introduction to elementary particles; 2nd rev. version. Physics textbook. Wiley, New York, NY, 2008.

2. Gordon L. Kane. Modern elementary particle physics: the fundamental particles and forces? Addison-Wesley Pub, updated ed edition, 1993.

3. Susmita Bhaduri, Anirban Bhaduri, and Dipak Ghosh. A new approach of chaos and complex network method to study fluctuation and phase transition in nuclear collision at high energy. The European Physical Journal A, 53(6):135, Jun 2017.

4. S. Bhaduri, A. Bhaduri, and D. Ghosh. Clan-model of particle production processrevisited in chaos-based complex network scenario. Physics of Particles and Nuclei Letters, 15(4):446–455, Jul 2018.

5. Susmita Bhaduri, Anirban Bhaduri, and Dipak Ghosh. Symmetry-Scaling Based Complex Network Approach to Explore Exotic Hadronic States in High-Energy Collision. 2019.

6. M. Newman. Networks: An Introduction. OUP Oxford, 2010.

7. Rudolph C. Hwa and Qing-hui Zhang. Fluctuation of voids in hadronization at phase transition. Phys. Rev. C, 62:054902, Sep 2000. 8. K. A. Olive et al. Review of Particle Physics. Chin. Phys., C38:090001, 2014.

9. Susmita Bhaduri and Dipak Ghosh. Study of void probability scaling of singly charged particles produced in ultrarelativistic nuclear collision in fractal scenario. Advances in High Energy Physics, 2016:1–5, 01 2016"

lanet-2019

DINÁMICAS Y NARRATIVAS DE LA GESTIÓN DE LOS ACUEDUCTOS COMUNITARIOS

La gestión de los Acueductos Comunitarios (AC) del borde urbano-rural del sur de Bogotá, como todos los AC comunitarios en Colombia, es el producto de los vínculos socioambientales que se van construyendo a partir de las acciones conjuntas entre los habitantes, sus organizaciones, y las instituciones públicas y privadas en los territorios. Estos vínculos socioambientales tienen características materiales y simbólicas, donde las personas, los elementos naturales y culturales y los procesos hacen parte integral de la gestión de los AC. Las características materiales se refieren a los vínculos que se establecen entre las personas y sus instituciones, los seres vivos y el agua, a partir de relaciones biológicas, ecológicas y con los objetos producidos por la cultura, para el acceso, la captación, la distribución, el uso y la disposición del agua. Las propiedades simbólicas de los vínculos, por su parte, se atribuyen a los aspectos que dan valor y significado a los elementos materiales y permiten la coordinación y la colaboración entre las personas que participan del proceso. Estos vínculos se refieren a los conocimientos individuales y colectivos, a los valores, acuerdos y reglas que se establecen entre los habitantes de un territorio y sus organizaciones para el uso, apropiación y disposición del agua.

A partir este enunciado, estudiamos los vínculos socioambientales de los AC del borde urbano-rural del Sur de Bogotá, tomado sus propiedades materiales y simbólicas en el desarrollo de su gestión, a partir de los archivos de 10 años (2005-2015) de acompañamiento a este proceso por parte de la Empresa de Acueducto de Bogotá."

Dolly Cristina Palacio and Rafael Hurtado21

FACULTAD DE CIENCIAS SOCIALES, UNIVERSIDAD EXTERNADO DE COLOMBIA DEPARTAMENTO DE FÍSICA, UNIVERSIDAD NACIONAL DE COLOMBIA

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lanet-2019

Uncovering the scaling behaviour of road accidents in urban environments

Following the tradition inherited from biological systems, it is typically hypothesised that environmental, economic and social properties of a city scale as a power-law of the city population size. For example, economic productivity, walking speed, criminal activity, CO2 emissions, the average number of social contacts and the number of people migrating to a city, have all been found to scale as a power-law of city size. In an attempt to contribute to the development of a quantified science of cities, it is shown here that the number of serious and fatal road accidents in English, Spanish and US cities, with the exclusion of the so-called dragon-king cities, can also be modelled as a superlinear power-law of the city population size of the form N(P) = aP^b, where b > 1 is known as the scaling exponent, N is the number of road accidents in a given city of population P and a is a proportionality constant. This means that, as P increases, N also increases but it does so faster than proportionally. More specifically, fatal accidents, which are analysed for the case of England and the US, scale with a clear superlinear behaviour. Serious accidents in England and Spain, however, display a weaker superlinear trend. These findings promote the question as to why such scaling behaviour exists, the answer to which will lead to more sustainable urban policies. While the higher tra�c congestion in bigger cities would have an effect in reducing the average driving speed and consequently, the number of severe road accidents; it is postulated here that it is also the higher tra�c congestion in densely populated areas ---among other features typically arising in urban environments--- that lead to higher levels of stress in drivers and therefore, to more accidents.

Carmen Cabrera Arnau and Steven Bishop

Department of Mathematics, University College London, London, Gower Street, WC1E 6BT

lanet-2019

Difusión en Redes Multicapa: Una Aplicación a la Transmisión de Enfermedades Infecciosas

El mundo actual está altamente conectado. Ejemplo de ello son los sistemas de transporte. Éstos pueden ser estudiados mediante ciencia de redes. Recientemente se ha encontrado que los sistemas de transporte pueden ser modelados como una red multicapa, las cuales tienen ventajas respecto de las monocapa, pues conservan más información sobre la estructura y comportamiento del sistema.

Actualmente, debido a la conectividad de las redes de transporte las enfermedades son propensas a propagarse más rápidamente. Por está razón es importante conocer más sobre este fenómeno.

En este trabajo, se estudió el fenómeno de difusión de agentes infecciosos a través de rutas aéreas para entender cómo es afectada la difusión bajo modificaciones en la estructura de dicha red.

Alejandra Yamamoto-Elizalde, Guillermo de Anda-Jáuregui and Enrique Hernández-Lemus2;4 2;31

Facultad de Ciencias, UNAM Departamento de Genómica Computacional, INMEGEN Centro de Ciencias de la Complejidad, UNAM Programa de Cátedras CONACyT para Jóvenes Investigadore

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En nuestro análisis, la red monocapa de transporte aéreo corresponde a una aerolínea y está conformada por aeropuertos representados por nodos y rutas de la aerolínea representadas por enlaces. La información de las rutas aéreas fue obtenida de la base de datos pública de OpenFlights.

Un conjunto de aerolíneas puede modelarse como una red multicapa. Con esta idea se analizaron diferentes comportamientos de propagación de enfermedades, pues muchas veces no es posible llegar a un destino mediante una compañía y se requiere de distintas aerolíneas.

La difusión se modeló con un caminante aleatorio y se analizó el comportamiento de la difusión en tres estructuras de red distintas:

1. Multiplex global 2. Multiplex de las alianzas Star Alliance, OneWorld y Sky Team 3. Conjunto de redes monocapa

Los resultados indican que se visitan (contagian) más aeropuertos en el multiplex global que en los otros casos. En cuanto al análisis de los multiplex correspondientes a las alianzas se obtuvo una correlación entre el número de aeropuertos visitados (contagiados) y su alcance global.

Esta primera aproximación al estudio de la difusión de agentes infecciosos en redes multicapa, permitirá desarrollar modelos que ayuden a limitar la propagación de enfermedades en el mundo.

lanet-2019

Using Networks Science to analyze Guardiola’s Barcelona

During the last decade, Network Science has become one of the most active fields in applied physics, due to the fact that there is a diversity of social, biological and technological systems that can be analysed under the scope of this, relatively new, branch of science. From the diversity of applications of Network Science, in this contribution we are concerned about its potential to analyze one of the most extended group sports: Football. Network Science allows addressing different aspects of the team organization not captured by classical analyses based on the performance of individual players. The recent ability of obtaining datasets of all events occurring during a match, including the position of the players and the interactions between them has opened the door to new kind of studies analysing and quantifying the behaviour of a team as a whole, together with the role of a single player. Under this framework, the organization of a team can be considered as the result of the interaction between its players, creating passing networks, which are directed (i.e., links between players go in one direction), weighted (i.e., the strength of the links is based on the number of passes between players), spatially embedded (i.e., the Euclidean position of the ball and players is highly relevant) and time evolving (i.e., the network continuously changes its structure). In this contribution we will show how it is possible to analyze the game of a football team using concepts from Network Science [1]. Specifically, we concentrate on the analysis of the 2009/2010 F.C. Barcelona, which was coached by Pep Guardiola. We show how networks parameters (such as shortest path, clustering coe�cient, algebraic connectivity or centrality dispersion) evolve in time, are constrained by spatial boundaries and are linked to the opponents’ networks. Interestingly, the combination of the certain network parameters allows assigning a particular signature to Guardiola’s Barcelona and opens the possibility of using Network Science as a proxy of team performance.

Javier M. Buldu , Xavier Busquets, Ignacio Echegoyen , Francisco Seirul.Lo and David Garrido

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Laboratory of Biological Networks, Center for Biomedical Technology, Universidad Politécnica de Madrid, Madrid, Spain Complex Systems Group and GISC, Universidad Rey Juan Carlos, Móstoles, Spain Grupo Interdisciplinar de Sistemas Complejos, Madrid, Spain ESADE Business School, Barcelona, Spain F.C. Barcelona, Barcelona, Spain.

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[1] J.M. Buldú, J. Busquets, J.H. Martínez, J.L. Herrera-Diestra, I. Echegoyen, J. Galeano and J. Luque, Frontiers in Psychology 9:1900 (2018).

lanet-2019

Evolución temporal de la investigación en física, análisis bibliométrico aplicando el formalismo de las redes complejas.

El siguiente trabajo se enfoca en el estudio de la evolución temporal del conocimiento mediante herramientas bibliometricas aplicadas en las palabras clave de los artículos científicos. El estudio de la creación y el uso de las palabras clave dan cuenta de la aparición de emergencias asociadas a descubrimientos. El formalismo de Redes Complejas permite conocer e interpretar la relación de interacción entre los estados microscópicos (palabras clave) mediante medidas topologícas de los grafos que dan cuenta de la estructura y dinámica del sistema, además está fuertemente basado en desarrollos e interpretaciones físicas, permitiendo entonces conectar las propiedades microscópicas y macroscópicas de los sistemas. Palabras claves: Redes complejas, Evolución temporal, Biblométria, Palabras claves (PACS), sistemas complejos.

Olga Barrios and Rafael Hurtado

Universidad Nacional de Colombia

lanet-2019

Gamification for decision making analysis

In recent years we’ve faced major airborne diseases outbreaks with farm animals origin and while there’s an intrinsic biosecurity risk is important to take the correct prophylactic measures and thus is imperative to know the best way to present information to decision makers in order to propitiate such measures. In this study we present a gamification strategy in order to detect different behavioral patterns and the most effective information treatments

Ollin Langle and Nick Cheney1;2 1;2

The University of Vermont, Department of Computer Science, Burlington, VT, USA The University of Vermont, Complex Systems Center, Burlington, VT, USA {olangle,ncheney}@uvm.edu

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lanet-2019

El siguiente trabajo se enfoca en el estudio de la evolución temporal del conocimiento mediante herramientas bibliometricas aplicadas en las palabras clave de los artículos científicos. El estudio de la creación y el uso de las palabras clave dan cuenta de la aparición de emergencias asociadas a descubrimientos. El formalismo de Redes Complejas permite conocer e interpretar la relación de interacción entre los estados microscópicos (palabras clave) mediante medidas topologícas de los grafos que dan cuenta de la estructura y dinámica del sistema, además está fuertemente basado en desarrollos e interpretaciones físicas, permitiendo entonces conectar las propiedades microscópicas y macroscópicas de los sistemas. Palabras claves: Redes complejas, Evolución temporal, Biblométria, Palabras claves (PACS), sistemas complejos.

Social cohesion emerging in human systems of a community-based physical activity program: Towards a planetary Health

Background: Planetary health locates health in the dimension of the human system as the interplay among individuals’ attributes, behaviors, and social interactions. The Recreovía, a community-based intervention, promotes physical activity and exemplifies a human system in natural environments like parks. This paper aims to study the social dimension of this program: determining the main stakeholders, and the emerging social cohesion.

Methods: For studying the human system, with the Recreovía Facebook data, we identified the main stakeholders and communities embedded in the online social network of the program. Then, by applying a Time Windows in Networks Algorithm, we detected relevant events on the Facebook temporal network of three specific program locations Finally, we assessed the network cohesion growth using a scaling model through the detected time windows.

Findings: Physical activity instructors were the main stakeholders (20•84% nodes of the network). Exemplifying emerging cohesion, we found: 1) Increment of Facebook users (43 to 272 nodes), friendships (55 to 2565 edges), clustering coe�cient (0•185 to 0•212), and density (0•04 to 0•07), 2) no preferential attachment behavior, and 3) a social cohesion superlinear growth with 1•73 new friendships per joined user.

Interpretation: The network analysis of the Recreovía underscores the influence of physical activity instructors and the critical periods of innovation where cohesion emerges as a co-benefit of the program. This analysis exemplifies a systemic approach that incorporates the interaction between the social and healthy behavior dimensions of a program occurring in parks.

Ana Maria Jaramillo, Felipe Montes and Olga L. Sarmiento

Universidad de los Andes, Bogotá Colombia

lanet-2019

In recent years we’ve faced major airborne diseases outbreaks with farm animals origin and while there’s an intrinsic biosecurity risk is important to take the correct prophylactic measures and thus is imperative to know the best way to present information to decision makers in order to propitiate such measures. In this study we present a gamification strategy in order to detect different behavioral patterns and the most effective information treatments

Nonlinear effects in random walks

Random walks are a very powerful tool to characterize the relation between complex networks structure and functionality. In fact, the trajectories displayed by random walkers have been widely used to understand several real-world complex systems. Historically, the vast majority of the models assume that the trajectories of the random walkers are unbiased, so the probability of moving to a specific node is independent of any of its attributes.

In recent years several articles have explored the dynamical effects of including diverse biases in the trajectories of the walkers[1, 2, 3, 4]. Specifically, in [4], the authors assume that the probability that a random walker moves to one node depends on its actual state characterized by its occupation probability. There, by stregthening this bias, some interesting non-linear phenomena are reported such as period-doubling bifurcations or first-order transitions with multistable solutions. However, these discrete-time nonlinear random walks were used in small toy topologies leading important questions unsurveyed for more realistic networks.

Here, we address this problem by extending the former work to account for larger networks. This allows us to better understand the nature of the transitions previously described. Slight variations of the bias parameter can induce drastic changes in the set of accessible nodes. This fact, along with other features, will be discussed in more details in this poster contribution.

Adriana Reyna Lara, David Soriano Paños and Jesús Gómez Gardeñes

GOTHAM lab, Institute for Biocomputation and Physics of Complex Systems (BIFI), University of Zaragoza, 50018 Zaragoza, Spain. Departamento de Física de la Materia Condensada, Universidad de Zaragoza, 50009 Zaragoza, [email protected], [email protected], [email protected]


[1] J. Gómez-Gardenes and V. Latora. “Entropy rate of di�usion processes on complex networks”. Physical Review E Vol. 78. Nm. 6 (2008), p. 065102.

[2] R. Sinatra et al. “Maximal-entropy random walks in complex networks with limited information”. Physical Review E Vol. 83. Nm. 3 (2011), p. 030103.

[3] G. Cencetti et al. “Reactive random walkers on complex networks”. Phys. Rev. E Vol. 98 (5 2018), p. 052302.

[4] P. S. Skardal and S. Adhikari. “Dynamics of Nonlinear Random Walks on Complex Networks”. Journal of Nonlinear Science (2018), pp. 1–26.

lanet-2019

Multiresolución Espacial en el Análisis de Redes de Datos Complejas

Monitorear el comportamiento de una red de datos es el punto de inicio para su análisis, debe ser una actividad constante que permita a operadores y administradores rapidamente notar cambios en la red. Dado el gran número de nodos de las redes actuales, es indispensable aplicar técnicas que posibiliten disminuir la complejidad de su análisis. En este artículo presentamos los resultados de la aplicación de la “Graph Wavelet Transform” en la obtención de una representación a multiples escalas espaciales de la topología de una red , enfocandonos en la detección de zonas 2 congestionadas. Para ello estructuramos una red multicapa compuesta de dos capas, una capa superior que monitorea o sensa y una capa inferior que representa la red de datos, que es la red a ser analizada. Cada nodo de la red de la capa superior tiene asociado un conjunto de nodos de la red monitoreada. El análisis de multiresolución se le aplica a la red de la capa superior logrando tener vistas “burdas” (representaciones con pocos nodos) y vistas “detalladas” de la topología; de esta manera se busca, inicialmente cambios de tráfico en la vista “burda”; de ser detectado algún posible cambio que se aleje de un comportamiento normal, se procede a realizar un análisis en la vista más detallada para determinar la zona de cambio o zona de congestión. El valor agregado de la representación en multi capa permitió realizar dos simplificaciones en la detección de zonas congestionadas, la primera se relacionada con la disminución de nodos evaluados, al considerar sólo los nodos de la red de la capa superior o red sensor y la segunda disminución se logra cuando el análisis de congestión inicialmente toma las topologias con menor número de nodos obtenidas del análisis de multiresolución.

Luz Angela Aristizábal, Nicolás Toro

Universidad Nacional de Colombia, Sede Manizales. {laaristizabalq,ntoro}@unal.edu.


1. D Shumman, S. Narang, P.Frossard, A. Ortega, and P. Vanderghesynst, “The Emerging Field of Signal Processing On Graphs: Extending High- Dimensional Data Analysis to Networks and Other Irregular Domains”. IEEE Signal processing Magazine. pp 83 – 98. (2013)

2. A. Sandryhaila and F. Moura, “Discret Signal Processing: on Graph”. IEEE Transacion signal processing. Vol 61. No 7. pp 1644 -1656, (2013).

3. N. Le Magoarou, J. Paratte, D. Shuman, V. Kalofolias, P. Vanderghesynst, and D. Hammond, “GSPBOX: A toolbox for Signal Processing on Graphs”, ArXiv e-prints. (2016). [Online] Available from: https://arxiv.org/abs/1408.5781v2 4. M. Newman, A-L Barabási. The strucsture and Dynamics of Networks. Princeton University Press. (2006)

5. G. Caldarelli, A. Chessa. Data Science & Complex Network. Oxford University Press. (2016).

6. S. Boccaletti , G. Bianconi , R. Criado, C.I. del Genio, J. Gómez-Gardeñesi, M.Romance, I. Sendiña-Nadal, Z. Wang. The structure and dynamics of multilayer networks. Elsevier. (2014)

lanet-2019

Análisis automático de casos

En este documento se aplica una metodología para el análisis de información no estructurada que tiene por objetivo la extracción y relacionamiento automático de información clave para la estructuración de un caso o un fenómeno criminal. Se aplican tres métodos de extracción de información, el método basado en frecuencias, un método heurístico y uno de inteligencia artificial. Los resultados muestran que el método heurístico y el de inteligencia artificial tienen un desempeño similar en la identificación de nombres mientras que el basado en frecuencias detecta entidades relevantes que complementan el análisis

William Lizarazo

Fiscalía General de la Nación, Colombia. {[email protected]}

lanet-2019

Un modelo de la dinámica de contagio de un entomopatógeno dentro de metapoblaciones de insectos plaga para el diseño de planes de manejo regional

El gusano cogollero del tomate (GCT) (Tuta absoluta) es una plaga que causa grandes pérdidas en el cultivo de tomate a escala global. En Colombia, además de causar pérdidas, el GCT requiere de la aplicación de grandes cantidades de insecticidas en cultivos de invernadero. Como una alternativa de control sostenible, AGROSAVIA2 desarrolló un bioplaguicida, a base del granulovirus de Phthorimaea operculella (PhopGV), el cual podría ser utilizado de tal forma que genere epidemias persistentes en el espacio y en el tiempo, reduciendo así la necesidad de intervenir el sistema agrícola constantemente.

El presente proyecto pretende favorecer el control de la plaga a escala regional a mediano y largo plazo, a través de la identificación de invernaderos clave para la aplicación del virus PhopGV y su posterior dispersión en la región por individuos infectados. Para ello se va a construir un modelo de red metapoblacional del GCT en una región de alta producción de tomate de invernadero en Colombia. Se busca que el modelo incluya explícitamente la estructura espacial del ambiente, el comportamiento del insecto, el estado fenológico de los cultivos y la participación de los agricultores en su control. En la red, los nodos representan sub-poblaciones del GCT en un invernadero. Las conexiones entre las sub-poblaciones representan los flujos migratorios entre estas y se encuentran caracterizados por la velocidad y dirección del viento entre sub-poblaciones y la distancia entre las sub-poblaciones. Adicionalmente, cada sub-población cuenta con atributos espaciales, y de estado fenológico del cultivo en el cual se encuentra localizada. Entendiendo estos comportamientos se plantearán las ecuaciones que describan la dinámica de movimiento y las dinámicas de contagio entre las subpoblaciones.

Felipe Montes, Martha Blanco-Silva, Hugo Fernando Rivera-Trujillo, Felipe Borrero-Echeverry, Diego F. Rincon

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Social and Health Complexity Center, Departamento de Ingeniería Industrial, Universidad de los Andes, Colombia. Centro de Investigación Tibaitatá, Corporación Colombiana de Investigación Agropecuaria -AGROSAVIA, Colombia.

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Modular Decomposition Applications to Network Analysis

We consider different applications of Gallai’s graph Modular Decomposition Theory [1] to network analysis. For a variety of discrete structures such as graphs, it is possible to define a decomposition based on the substitution of structures in a nested schema. Those structures can be decomposed as an outer structure, with each of whose components represent, in turn, internal structures (see [2, 3]). In the case of undirected graphs G = (V, E), such a decomposition yields a hierarchical structure which can be thought of as a tree. We will refer to it as the Modular decomposition (MD) of a graph. A module M � V satisfies for each v � V – M, that either v is connected to all b � M, or it is not connected to any. Children of any given module M will be in turn modules of M. Type-tags indicate the type of relations among these modules and can be of three types: complete (all elements are related), empty (no elements are related) and prime (any other case). Classical well-known results (see [4] for a review), establish that the MD of an undirected, simple graph is unique, so our approach establishes then a well-defined context for hierarchical modeling by using the MD, which is known that can be computed in linear time [5]. We present here how to factorize (decompose) a graph into modules via operad theory (for a thorough review in operad theory, see [4]) into a tree representation (see Fig.1), and some of the applications of those representations: from understanding structural and functional similarities among nodes in a network (e.g. Structural Equivalence [6]), biological analysis (e.g. analysis of protein-protein interaction networks [7] and brain networks [8, 9]) to complexity, among others, to proposing a model for random graphs based on this decomposition [9].

Nicolás Bolívar , Miguel Méndez and Carenne Ludeña1;2 3 4

Centro de Investigación en Ciencias Básicas y Aplicadas, Universidad Antonio Nariño, Bogotá, Colombia. Facultad de Ciencias, Universidad Antonio Nariño, Bogotá, Colombia. Escuela de Matemáticas y Ciencias Computacionales, Yachay Tech, Urcuquí, Ecuador Matrix CPM Solutions, Bogotá, [email protected], {mmendezenator, carinludena}@gmail.com

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S T R U C T U R E

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R E F E R E N C E S :1. Gallai, T. Transitive orientabare graphen. Acta mathematica Hungarica 18, 25–66 (1967).

2. Möhring, R.H., Rademacher, F.J. Substitution decomposition for discrete structures and connections with combinatorial optimization. Annals of Discrete Mathematics 19, 257-356 (1984).

3. Möhring, R.H. Algorithmic aspects of the substitution decomposition in optimization over relations, set systems and boolean functions. Annals of Operations Research, 4(1), 195 -225 (1985).

4. Méndez, M. Set Operads in Combinatorics and Computer Science. Springer Briefs in Mathematics. Springer, Heidelberg (2015).

5. Habib, M., De Montgolfier, F. & Paul, C. (2004). A Simple Linear-Time Modular Decomposition Algorithm. In: Hagerup T., Katajainen J. (eds) Algorithm Theory - SWAT 2004. Lecture Notes in Computer Science, vol 3111, pp. 187-198. Springer, Heidelberg (2004).

6. Brandes, U., Lerner, J. Role-equivalent Actors in Networks. In: Obiedkov S, Roth C (eds). ICFCA Satellite Workshop on Social Network Analysis and Conceptual Structures: Exploring Opportunities (2007).

7. Gagneur, J., Krause, R., Bouwmeester, T., Casari, G. Modular decomposition of proteinprotein interaction networks. Genome Biology, 5(8) R57, 1-12 (2004). 8. Méndez, M., Ludeña, C., Bolívar, N. Hierarchical modeling of graphs using modular decomposition. In: Frontiers Human Neuroscience Conference Abstract: 2nd International Neuroergonomics Conference. doi: 10.3389/conf.fnhum.2018.227.00006

9. Ludeña, C., Méndez, M., Bolívar, N. Modular decomposition of graphs and hierarchical modeling. In: https://arxiv.org/abs/1811.10705v1. Last accessed 2019/04/14.

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