DYSONET: A Study of NetworksHuman behaviour through dynamics of complex social networks: An interdisciplinary approach

Sixth (6th) Framework ProgramSTREP Project (STREP=Specific Targeted REsearch Project)

Panos ArgyrakisUniversity of ThessalonikiThessaloniki, GREECE

FP6 (EC part): Three Main Blocks of Activities

Block 1: Focusing and Integrating European Research

7 Priority Thematic AreasSpecific activities Covering a Wider

Field of Research

1.Life sciences, genomics and biotechnology for health

2. Information society technologies

3. Nanotechnologies and nano-sciences, knowledge-based functional materials, new production processes and devices

4. Aeronautics and Space

5.Food quality and safety

6. Sustainable development, global change and ecosystems

7.Citizens and governance in a knowledge-based society

Research for policy support

New and emerging science and technologies (NEST)

Specific research activities for SMEs

Specific international co-operation activities

Block 2: Structuring the ERABlock 3: Strengthening the

foundations of ERA

Research and Innovation

Human resources & mobility

Research infrastructures Science and society

Co-ordination of research activities

Development of research/innovation policies

7 THEMATICAREAS

(e.g. Nano, IST, etc)

CROSS-CUTTINGACTIVITIES

New and Emerging Scienceand Technology (NEST)

……..

PATHFINDERInitiatives: ADVENTUREINSIGHT

Tackling Complexityin Science

DYSONET

SyntheticBiology

What it meansto be human

2003 Call:

This NEST: Tackling Complexity in Science(only 1 call, Ever !!)

• Early stage funding of emerging research areas

• Must be interdisciplinary

• Must help identify and coordinate the community working on such problems by providing means of interaction

• Fashionable areas: Biology, Social sciences, Environment

• Originally planned for funding: 6-10 STREPS & 1 CA

• Finally approved 11 out of 40 STREPS, 1 out of 3 CA

• Criteria used for evaluation: Relevance, Excellence, Impact, Consortium, Financial aspects, Management

The consortiumNo Organisation name Group

LeaderAbbr. Town Country

1 Aristotle University of Thessaloniki P. Argyrakis AUTH Thessaloniki Greece

2 Bar-Ilan University S. Havlin BIU Ramat-Gan Israel

3 Stockholm University F. Liljeros SU Stockholm Sweden

4 Justus-Liebig-Universitaet Giessen A. Bunde JLUG Giessen Germany

5 Universidade de Aveiro J.F. Mendes UA Aveiro Portugal

6 Instituto Nazionale per la Fisica della Materia R.Mantegna INFM Palermo Italy

7 Boston University H.E.Stanley BU Boston USA

Geographical distribution

BU

SU

JLUG

UA

INFMAUTH

BIU

Duration of the project : 36 months14.12.2004 – 13.12.2007

• “Coordination” means that EC wants to “see” and have only one (1) contact point, that of the Coordinator

• Send any and all materials you have to the Coordination point only (Thessaloniki), NOT to Brussels directly

• Project has a Project officer in Brussels: Dr. A. Martin-Hodbey

• Consortium Agreement

• e.g. IPR agreement (Intellectual Property Rights)

• Ethical issues

• NCP (National Contact Point) for every country

BudgetOrganisation name Costs EC contribution

1 Aristotle University of Thessaloniki 310,000.00 310,000.00

2 Bar-Ilan University 302,000.00 302,000.00

3 Stockholm University 202,000.00 202,000.00

4 Justus-Liebig-Universitaet Giessen 202,000.00 202,000.00

5 Universidade de Aveiro 202,000.00 202,000.00

6 Instituto Nazionale per la Fisica della Materia 202,000.00 202,000.00

7 Boston University 158,798.00 0.00

TOTAL 1,578,798.00 1,420,000.00

RTD Activities: 1,488,798.00 Management activities: 90,000.00

Indicative budget breakdown

Category Percentage

Personnel 51.70 %

Travel 13.68 %

Equipment 9.24 %

Consumables 3.43 %

Management 5.28 %

Overhead 16.67 %

TOTAL 100.00 %

WorkplanWork-

packageWorkpackage title Lead

contractorPerson-months

Startmonth

Endmonth

Deliverable

WP1 Data collection SU 50 0 18 D3, D4, D5

WP2 Network model characterization BIU 97 0 24 D8, D9

WP3 Designs for network optimization UA 106 6 24 D6, D10

WP4 Analysis of real-world networks JLUG 123 12 36 D7, D11, D13

WP5 Dissemination of results INFM 41 3 36 D1, D12, D14

WP6 Management AUTH 15 0 36 D2, D15

TOTAL 432

Months 6 12 18 24 30 36

WP1

WP2

WP3

WP4

WP5

WP6

Data collection

Network modelcharacterization

Designs for networkoptimization

Analysis ofreal-world networks

Disseminationof results

Management

DeliverablesDel.No

Deliverable name WP no. Leadparticipant

Estimated person-months

Disseminationlevel

Delivery date

D1 Web site with project information and project results WP5 6 10 PU 3

D2 A web-based project repository WP6 1 10 PU 4

D3 Databases of social contacts WP1 3 20 CO 12

D4 Databases of economic network WP1 3 15 CO 18

D5 Databases of environmental networks WP1 3 15 CO 18

D6 Details and tools for optimal network design WP3 5 53 PU 18

D7 Details for the formation and evolution mechanisms of social networks

WP4 4 52 PU 18

D8 Values of critical threshold of a network, for protection against attacks and failures

WP2 2 49 PU 24

D9 Values of the optimal path on a network WP2 2 48 PU 24

D10 Method for restructuring of a network, so that critical properties behave in a controllable way

WP3 5 53 PU 24

D11 Recipe for obtaining the most efficient network WP4 4 33 PU 30

D12 Organization of Conference and Workshops WP5 6 22 PU 30

D13 Application of the results to other networks, such as economic networks

WP4 4 38 PU 36

D14 Publications in journals and conference proceedings WP5 6 9 PU 36

D15 A summary of all management actions during the course of the project

WP6 1 5 PU 36

432

DYSONET Summary

• Develop the basic science of complex social networks in a quantitative way,

• Apply our findings to the dynamics of human behavior, and generalize to a wider range of networks, including economic, traffic, and environmental networks.

• Methods and techniques based on Statistical Physics concepts• Quantitative characterization of complex social networks by analyzing

a number of real-world phenomena• Dynamic patterns in other disciplines, such as Economics and Finance,

and Environmental networks. • Characterize, optimize and control the structure, dynamics and flow in

complex social networks• Our studies will use extensive real-world data of social nature, which

will be collected in the frame of the project.

Objectives

• To focus and develop further the science and applications of the dynamics of complex social networks

• To apply similar techniques and investigate 2 or 3 other key systems, such as networks in econophysics, traffic and environment, thus showing the generality of our methods

• To provide our developed tools and methods to other fields, by making them widely available.

Methodology(1) Real-world data collection

• Collect large scale real-world data for social networks in Sweden:– daily social contacts (target: 8 million individuals)– flow of patients between and within hospitals (target:

300000 people)– sexual relationships (target: 10000 people)

• Email, collaboration and trust networks data• Environmental network• Finance data. Networks among firms or assets.

Possibility to detect alternative ways to model factual connections among firms.

Methodology(2) Network model characterization

• Develop network models and characterize critical properties of these networks:– Robustness against attacks and failures. Critical threshold. Percolation

theory against different types of attacks.– Capability for network flow. “Optimal path” on the network: length, the

time or energy or price needed to traverse it.• Statistical methods such as scaling theory, percolation theory, critical

phenomena, and renormalization approaches to analyze the structure, stability and dynamics of different network types under conditions of random and intentional attack.

• Applications to social networks, as well as to the disruption and protection of networks.

• Extensive large-scale computer simulations to determine static and dynamic properties. Use of Grid resources!

• Develop new analytical, numerical and simulation techniques which will allow us to effectively characterize the models.

Methodology(3) Designs for network optimization

• Identify designs of network models which are optimized for robustness and flow.

• Initially, identify designs which optimize these quantities independently.

• For robustness, networks which are optimized against simultaneous attacks and random failures.

• Define a metric which represents a combination of these quantities and then identify designs which optimize both of robustness and flow simultaneously.

• We will also develop strategies to improve stability and transport in a given network.

Methodology(4) Analysis of real-world networks

• Apply our finding of the phases 2 and 3 to the real world networks of phase 1, and generalize.– Identify and validate network model. Static properties

of the network are consistent with the model.– Determine network robustness and flow capability.

Compare against optimal designs. Are real world networks close to optimal design? If not, what principles drive the network to its design?

– Proposal of network formation mechanisms. Decisions made locally and determine the overall network structure. The key will be to identify information available locally to individuals and the decisions they make.

• Determine additional underlying principles common to all systems. Focus on:– Origins of the collective human behavior. What characteristics

cause collective behavior? Key parameters characterizing connections and interactions? For what values of these parameters does collective behavior emerge?

– Patterns of collective human behavior. What types of patterns in space and time are formed by the collective behavior?

– Optimization of emergent collective behavior. To what extent does the collective behavior optimize key variables? How can parameters of a system be changed to meet optimization goals?

Methodology(4) Analysis of real-world networks

Methodology(4) Analysis of real-world networks

• Expand to other systems:– Similar questions for Econophysics:

• Human behavior through stocks trading.• Correlation-based networks of the market portfolio.• Topology of efficient stock exchanges and emerging ones.• Same analysis will be attempted in some commodity market.

– Compare social and economic networks. Common methods? Common features? If the results are encouraging, extend to other types of networks, as well.

Real-world phenomena to study

• Crowd behavior: strategies to evacuate people and stop panic.

• Search strategies: efficient networks for searching objects and people.

• Traffic flow: optimization of collective flow.• Dynamics of collaboration: human relationship networks

such as collaboration, opinion propagation and email networks.

• Spread of epidemics: efficient immunization strategies.• Patterns in economics and finance: dynamic patterns in

other disciplines, such as Economics and Finance, and Environmental networks.

Crowd behavior• Panic-induced crowd stampedes.• Architects, engineers, etc• Statistical Physics approach. Strategy of an individual.• Understanding crowd network behavior towards

efficient approaches to evacuate crowds. Optimal design of large common areas. Mechanisms through which cooperation increases effectiveness. Important questions– “What effect does feedback from others have on the behavior

of an individual?”– “Can we effectively control the spread of panic by introducing

a system of geometrical constraints or sociological factors?”

Search strategies• Optimization of random searching.• Identifying strategies by which individuals can best

discover sparsely and randomly located food, for example.• Optimization of foraging behavior of many competing or

cooperating individuals. Help design effective collective search methods such as for searching for lost people.

• Possibility of searching individuals in society based on their personal properties and tendencies, Search in the virtual space of possible personal properties.

• Applications of such strategies may be the understanding of the formation of groups in society as well as understanding the process of searching for jobs, experts in a field, acquaintances and the like.

Traffic flow

• Traffic flow is an example of collective behavior. Drivers listen the same radio traffic reports. Emergence of collective behavior, causing even stronger bottlenecks on a previously optimal route.

• Traffic flow is but one example of flow as emergent collective behavior. Almost anything can “flow” in a complex system: Hinder some flows and optimize others. Studying the optimization of collective behavior in flow in complex systems has profound implications for a number of critical problems, such as the distribution of food and medical aid or the spread of epidemic disease.

Dynamics of collaboration• Collaboration takes place on an underlying network of

relationships.• Indirect methods of identifying relationships• We will characterize topology, dynamics of the network and flow

in the network and identify the organizing principles of the collective behavior.

• Determine if the networks represent an optimization of the critical parameters. Study the dynamics of propagation of new successful scientific concepts and ideas by using keywords and analyze their evolution with time in the network collaboration.

• Study collaboration patterns via indirect routes, e.g. Email network. Time evolution of the network in the aim to find patterns that will help modeling and better understanding of this network. How clustering is formed dynamically in the email network.

Spread of epidemics• Spreading of epidemics is an extremely important public

health issue. Absence of critical threshold. Clearly a collective phenomenon, where the underlying structure determines the fate of the disease evolution.

• Strategies of immunization.• Determine optimum immunization strategies, depending

on the particular structure of a given population, and interdependence of network topology and epidemics, i.e. restructure a given network to protect against a disease spread. Similar: enhancing the spread on such a network.

• It is similar to traffic flow, but e.g. the probability of spreading and the presence of immune nodes do not appear in the problem of traffic flow.

Patterns in economic and finance

• Markets are a collective phenomenon characterized by cooperation and competition, specialization and globalization.

• Emergent collective behavior in the economy is manifested in various ways, such as self-adaptation.

• Feedback mechanisms.• Significant open questions include:

– “How does one map out these structures from financial data?”– “What is the role of such structures in the feedback mechanisms causing major

changes on the market and how can those changes be quantitatively characterized and controlled?”

• We will investigate the statistical robustness of these networks by estimating quantitatively the effects of noise dressing on their stability.

• Dynamics of the portfolio (and/or of the entire market) both on elements which are acting as “hubs” of the financial networks and elements which are acting as branches of it. Conclusion about the “stability” of the considered portfolio could therefore eventually be extrapolated.

Potential Impact of DYSONET

• Applications are mainly related to the dynamics of human behavior• The basic science and the methods developed will be applicable to a broad range

of phenomena (e.g. the robustness of power grids, the spreads of rumors and epidemics, efficient immunization, efficient searching, etc).

• Important questions to be answered:(a) What are the fundamental properties that characterize the type of a network?(b) What mathematical models represent a given real world network?(c) How are stability, robustness, dynamics and transport properties of these models characterized?(d) What network design strategies can be developed to optimize the stability, robustness, and efficiency of transport on these networks?

• Other direct useful benefits:(a) Efficient approaches for immunization and panic control.(b) Design of optimal networks, for tasks such as searching people and distributing goods.(c) Optimal approaches for crowd evacuation during panic situations, such as in cases of earthquakes, terrorist attacks, etc.