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1 2010-01-27 NS CTA Seminar

The ARL Network Science Collaborative Technology

Alliance Research Goals and Year-1 Plan

Dr. Alexander Kott, CAM Dr. Will Leland, Program Director

NS CTA Seminar Series 27 January 2010


ARL NS CTA Research Goals & Plan

•  What is the ARL NS CTA? •  Research goals •  Status update •  Snapshot: the year-1 research plan


What is the Network Science Collaborative Technology Alliance?

•  A collaborative research alliance of academia, industry, and government –  Spanning organizations, technical

disciplines, and research areas •  Four closely collaborating Centers

•  Jointly interdisciplinary Cross Cutting Research Issues •  Parallel basic (6.1) and applied (6.2) research activities •  NS CTA Facility serves as the nexus for the Alliance

–  Significant and persistent staff at NS CTA facility –  Distributed multi-user experimentation –  Distributed connections among the Alliance

Focus: collaborative network science research

•  Initial funding awarded in September 2009 •  A 5-year program with a potential 5-year extension


NS CTA Research Goals

•  Perform foundational, cross-cutting research on network science to achieve: –  Fundamental understanding of the interplay and common

underlying science among social/cognitive, information, and communications networks

–  Determination of how processes and parameters in one network affect and are affected by those in other networks

–  Prediction, design, and control of the individual and composite behavior of these complex interacting networks

•  Relevant to the technical challenges of the Army mission • Resulting in:

–  Optimized human performance in network-enabled warfare –  Greatly enhanced speed and precision for complex military

operations


IRC CNARC INARC SCNARC

Principal Member BBN Tech Penn State

U Illinois- Urbana

Champaign (UIUC)

Rensselaer Polytechnic

Institute (RPI)

General Member

(HBCU/MI) UC-Riverside CUNY CUNY CUNY

General Member U Delaware USC UC-Santa

Barbara Northeastern

Univ

General Member ArtisTech UC-Davis IBM IBM

General Member UC-Santa Cruz

The Network Science CTA Consortium


Why Network Science?

•  The exceptional importance of networks transpired in the last 20 years, with powerful phenomena like the Internet and irregular warfare

•  Network Science is the youngest of recognized sciences – emerged only in the last 10 years (seminal paper: 1999)

•  Networks of all kinds--biological, social, computer--are in a unique class of creatures, which live their own mysterious lives: evolve, change, and behave in little-understood ways

•  Network science studies fundamental laws of evolution and behaviors of “living” networks, treating them as holistic organisms


What’s Different about this Program?

•  The first and only program to study mutual interdependency and relations of three dissimilar (and most influential) genres of networks: social, information, communications

•  The most powerful and Army-relevant phenomena emerge in intertwining of social, information, communications; not in one of them individually.

•  Uniquely organized as 4 collaborating centers to balance (a) in-depth expertise in each network genre and (b) relations, dependencies, and mutual influences of three genres.


First Steps

•  Initial Program Plan –  Cohesive research program –  Network science context –  Army relevance –  Inherent collaboration

•  Research project kick-offs •  Seminar series •  Educational coordination •  Experimental infrastructure coordination •  NS CTA Facility (Cambridge, MA)

–  First post-doc already here –  Remodeling underway


The NS CTA Facility

•  Networking: –  Ethernet to each desk –  100 Mbits/sec Internet access

• Experimentation tools: ‒ Lab with TBA simulation blades ‒ Experimentation management system for distributed CTA resources to be developed under 6.2 program

•  Size: 4,781.5 sq. feet •  Equipment: facility

printers and copiers, PC on desks as required

•  Access: –  Door keyed to badge –  Access to BBN’s

cafeteria and library


The Initial Program Plan (IPP)

Six major sections: •  CCRI: Trust in Distributed Decision Making (“Trust”) •  CCRI: Evolving Dynamic Integrated Networks (EDIN) •  Non-CCRI network science research

–  Interdisciplinary Research Center (IRC) –  Information Networks Academic Research Center (INARC) –  Social/Cognitive Networks Academic Research Center

(SCNARC) –  Communications Academic Research Center (CNARC)


CCRI: Trust in Distributed Decision Making

Coordinator: Sibel Adali, RPI Email: [email protected]

Government Lead: Jeff Hansberger, Email: [email protected]

Projects and Leads:

Project T1, Trust Metrics, Estimation, and Presentation: D. Agrawal, IBM (INARC)

Project T2, Trust impact on network: S. Adali, RPI (SCNARC)

Project T3, Network impact on trust: P. Mohapatra, UC Davis (CNARC)


Project T1: Trust Metrics, Estimation, and Presentation

Key Products:

models and methods for exploring the tradeoffs for different definitions of trust, a specification of one or more QoI dimensions that enable users to assess trustworthiness accurately, impact of network topology and dynamics on the cost/accuracy tradeoff for estimating these QoI dimensions.

atomic QoI operators and a consistent QoI algebra that allows flexible combination of these atomic QoI operators.

formal models of argumentation about trust and establish the properties of these models.

research software for visual analysis of trust patterns to develop a better understanding of user behavior

Research Foci: incorporating Trust as a dimension of Quality of Information (QoI); QoI estimation and enhancement; measuring human trust, representation of rationale for trust and presentation of trust

Key Researchers: D. Agrawal, IBM (INARC) R. Govindan, USC (CNARC M. Srivatsa, IBM (INARC) S. Parsons, CUNY (SCNARC) K. Haigh, BBN (IRC) J. Opper, BBN (IRC)

Key Technical Ideas:

model QoI as a probabilistic utility function using a joint distribution over different constituents of QoI,

different QoI trade-offs can be exposed by different classes of network topology

formulate a QoI algebra – a set of composable operators for computing different QoI attributes

the existing formal models of argumentation, developed to reason about belief in uncertain events, can be used to model and reason about human trust in information sources

trustworthiness depends on the chain of custody of the information, and on the context under which the information was gathered


Project T2: Trust Impact on Network

Key Products:

model and empirical predictions of the effects of variations in information provenance and the personal reputation of the information sources

an experimental testbed including a simulated multi-player task environment and EEG data collection

an algorithm for trust evaluation based on information from multiple sources

theory of how reciprocity emerges and the role of reciprocity in social networks

empirical analysis (using datasets for San Diego fire evacuation) and computational modeling of information propagation and evacuation patterns along trusted links

algorithms and research validation of distributed oracles for unified trust SA

Research Foci: identification of primary mechanisms with which trust changes the network: both its static and dynamic properties; economic and cognitive aspects of trust’s influence on network

Key Researchers: S. Adali, RPI (SCNARC) A. Goel, Stanford (CNARC) C. Lim, RPI (SCNARC) P. Pirolli, PARC (INARC) W. Gray, RPI (SCNARC) S. Zhu, PSU (CNARC) K. Haigh, BBN (IRC) J. Opper, BBN (IRC) J. Hansberger, ARL

Key Technical Ideas: economic models may represent function of trust in a network. In these models, trust acts as a risk management tool: the risk of obtaining incorrect information vs. the risk of not getting valuable information, the risk of interacting with an adversary vs. a friend. computation cognitive model of judgment of credibility can be fit to data from HCI studies by assuming a form of Random Utility Model with credibility assessments stochastically based on latent factors in a probabilistic semantic space representation of the information and the information sources

differences in trust are signaled by differences in cognitive brain mechanisms which can be detected by event-related brain potential (ERP) measures heterogeneity of nodes facilitates the information diffusion process and evacuation efforts balanced flows between nodes signal trust between individuals

trustworthy distributed oracles provide an integrated real-time coordinated view of trustworthiness and trust encompassing SCN, IN, and CN entities


Project T3: Network Impact on Trust

Key Products:

distributed algorithm for trust propagation that is resilient to errors caused by double counting or under-counting information.

new protocols that enable measurable values of trust in provenance, and methods to compute trust in provenance

algorithms that enhance trustworthiness of the information and hence self-boosting the quality of information networks

iterative algorithm for computing prominence and an initial implementation tested on a Twitter network dataset

Research Foci: the impact of the static and dynamic properties of networks of different types on the trust and trustworthiness of information or people in a framework; role of mobility; self-boosting trustworthiness; emergence of trusted communities and leaders

Key Researchers: P. Mohapatra, UC Davis (CNARC) K. Levitt, UC Davis (CNARC) S. Adali, RPI (SCNARC) D. Roth, UIUC (INARC) K. Haigh, BBN (IRC) J. Opper, BBN (IRC)


exploit utility of information, including how useful is delayed information and how quickly does its utility decay with time; similarly, how quickly does its utility decay with loss in accuracy and completeness.

use reputation system approach -- the assignment of trust values to a component (a user, a router, a host, etc.) as a function of the trust values determined by other components.

the trustworthiness of information can often be improved by the interconnected network itself, i.e., self-boosting of information networks.

community formation, impacted by prominence and prominence, is a self-boosting mechanism, community formation processes in leadership networks crowd out diversity unless they are accompanied with shared activities that foster diversity

distributed aggregation of different representations of trust assessments into a unified representation enables more accurate situational awareness

2010-01-27 15 NS CTA Seminar

CCRI: Evolving Dynamic Integrated Networks (EDIN)

Coordinator: Prithwish Basu, BBN Email: [email protected]

Government Lead: David Dent, ARL Email: [email protected]

Projects and Leads:

Project E1, Ontology and Shared Metrics for Dynamic Composite Networks: P. Basu, BBN (IRC); J. Hendler, RPI (IRC, SCNARC)

Project E2, Mathematical Modeling of Composite Networks: J. J. Garcia-Luna-Aceves, UC Santa Cruz (CNARC); P. Basu, BBN (IRC)

Project E3, Structure and Co-evolution of Composite Networks: A. Singh, UC Santa Barbara (INARC); B. Szymanski, RPI (SCNARC)

Project E4, Modeling Mobility and its Impact on Composite Networks: T. La Porta, Penn State University (CNARC)


Project E1: Ontology and Shared Metrics for Dynamic Composite Networks

Key Products:

Ontology for describing the relationships between communication, social and information networks and the possible dynamics changes and effects therein

Cross-cutting metrics for measuring the properties of composite military networks

Research Foci:

Shared vocabulary and ontology across social, information, and communication networks

Entities in a composite network, their attributes, relationships and how do they affect network formation

Modeling of temporal dynamics and stochastic behaviors in composite network evolution

Metrics defined across composite networks

Key Researchers:

P. Basu, BBN (IRC) J. Hendler, RPI (IRC, SCNARC) J. Bao, RPI (IRC, SCNARC) C. Partridge, BBN (IRC) W. Leland, BBN (IRC) A. Singh, UC Santa Barbara (INARC) A. Bar-Noy, CUNY (CNARC, INARC)


OWL provides expressivity for composite network structure; extensions will be needed to better represent the dynamic changes in networks over time

Extend influence diagrams – developed in decision theory for characterizing conditional relationships and dependencies (both deterministic and stochastic) between various variables in a system--to describe real networks and define shared metrics on them


Project E2: Mathematical Modeling of Composite Networks

Key Products:

Generative graph models for weighted, time-evolving graphs

Information network stream summarization methods in an evolving environment

Tensor models and algorithms for time-evolving graphs

Constrained optimization and power-law models applied to dynamic composite networks

Research Foci:

Mathematical representations, models, and tools to capture the salient aspects of dynamic composite networks and their evolution, including composite multilayered graph theory, tensor analysis tools, temporal graphlets, dynamic random graphs, constrained optimization

Key Researchers:

J.J. Garcia-Luna-Aceves, UCSC (CNARC) P. Basu, BBN (IRC) C. Aggarwal, IBM (INARC) A. Bar-Noy, CUNY (CNARC, INARC) I. Castineyra, BBN (IRC) C. Faloutsos, CMU (INARC) M. Faloutsos, UC Riverside (IRC) R. Ramanathan, BBN (CNARC) H. Sadjadpour, UCSC (CNARC) A. Singh, UCSB (INARC) D. Towsley, Umass (IRC) X. Yan, UCSB (INARC)


Create composite graph representations – efficient graph algorithms can potentially be extended to function efficiently (e.g., polynomial time) on certain composite graphs except for “rich” composite graphs in which algorithmic complexity may explode.

Capture “time” in graphs by recursive models, like Kronecker graphs and variations (RTG), to obtain realistic time-dependent models

Extend constrained optimization formulations of network formation to capture node mobility and channel dynamics


Project E3: Structure and Co-evolution of Composite Networks

Key Products:

Methods to represent relationships between information flow and network structure across multiple time-scales

Algorithms for the simulation of dynamical processes (particle–-network framework) in multi-scale networks in application to co-evolution, spreading and contagion

Scalable algorithms for modeling community dynamics and community detection, and associated analysis of their performance and scalability

Research Foci:

Temporal evolution of structural properties of integrated networks

Short-term effects of network stimuli and dynamics on the properties of a given composite network

How composite networks co-evolve in longer time scales due to exogenous and exogenous influences

Key Researchers: A. Singh, UCSB (INARC) B. Szymanski, RPI (SCNARC) L. Adamic, Michigan (INARC) N. Contractor, Northwestern (INARC, IRC) C. Faloutsos, CMU (INARC) J. Han, UIUC (INARC) P. Basu, BBN (IRC)


Time-resolved models of information spread to combine the structure of the communication network with topic modeling, and to predict the depth and path of information spread

Fractal distributions over multiple scales in space may approximate demographic, geographical and economic factors underneath the structure of these networks

Reaction-diffusion formulation on networks will allow us to explore the correlation feedback and co-evolution of the network structure and the equilibrium stationary distribution of particles and their flows.

Extend existing clustering methods for homogeneous information network to the environment of heterogeneous integrated networks

W. Leland, BBN (IRC) A.-L. Barabasi, NEU (SCNARC) H. Makse, CUNY (SCNARC) A. Pentland, MIT (SCNARC) A. Vespignani, Indiana (SCNARC) S. Wasserman, Indiana (SCNARC)


Project E4 - Modeling Mobility and its Impact on Composite Networks

Key Products:

Models of mobility that may be used to drive the evolution of social, information and communication networks. These models will provide synthetic traces, which may be used to generate network dynamics at the appropriate level of granularity and metrics

Research Foci:

Mobility models that capture metrics of specific interest to the evolution of different types of networks, and ultimately the evolution of composite networks

Key Researchers:

T. La Porta, PSU (CNARC) K. Psounis, USC (CNARC) P. Mohapatra, UC Davis (CNARC) A.-L. Barabási, NEU (SCNARC) P. Basu, BBN (IRC)


Empirical characterization of longitudinal data, covering a period of three months up to one year, from a network of millions of cell-phone users in an industrialized European country to analyze how a real social network changes over time

Use empirical data to develop tractable mathematical models to analyze instant and asymptotic performance of network metrics (e.g., connectivity and link stability) under each mobility model


Non-CCRI Research: Interdisciplinary Research Center (IRC)

Director: Will Leland, BBN Technologies Email: [email protected]

Government Lead: Ananthram Swami Email: [email protected]

Projects and Leads:

Project R1: Methods for Understanding Composite Networks: M. Faloutsos, UCR (IRC)

Project R2: Characterizing the Interdependencies Among Military Network Components: M. Dean, BBN (IRC); J. Hendler, RPI (SCNARC, IRC)

Project R3: Experimentation with Composite Networks: A. Leung, BBN (IRC); J. P. Hancock, ArtisTech (IRC)


Project R1: Methods for Understanding Composite Networks

Key Products:

methods for robust revealed preference elicitation

a formal definition of environment design for networked actors and an initial algorithmic approach

a new higher category for representing networks of heterogeneous networks

micro and macro meta-models for modeling composites of heterogeneous genres of networks

sampling methods to provide accurate and realistic structural information on (partially observable) networks

an initial toolset of clustering algorithms for realistic multi-genre networks

Research Foci: understanding and controlling integrated multi-genre networks using techniques outside classic structurally-focused approaches; initial approaches: economic (utility-based) models, category theory for compositional modeling of networks, and knowledge extraction from composite networks.

Key Researchers: M. Faloutsos, UCR W. Leland, BBN P. Basu, BBN I. Castineyra, BBN M. Kokar, NEU D. Parkes, Harvard J. Srivastava, U Minn. D. Towsley, U Mass. M. Wellman, U Mich.


modern mathematical economics provides well-developed techniques to model large systems of approximately rational agents as they compete and cooperate (with limited information) to deploy limited resources for value creation

implicit preferences of actors may be robustly inferred by a combination of passive integrated observations and active elicitation of their actions

category theory offers a mathematical framework that has proved to be very efficient in capturing and composing representations of multiple non-homogeneous structures

given a measured, but potentially inaccurate and incomplete, topology of an integrated network, one can extract knowledge of its structure using techniques such as graph sampling and clustering, based on understanding the interplay between network structure and function


Project R2: Characterizing the Interdependencies Among Military Network Components

Key Products:

algorithmic measures of the utility of information that take into account relevance, redundancy, and trust

methods for representing information loss in communications network and error in information networks for use in dynamic network models

definition of information loss and, if possible, entropy with respect to non-communication networks under varying semantic conditions.

models and algorithms for low-cost computation of information network utility measures that require input from human assessors

dynamic network metrics for assessing impact of loss and error on the socio-cognitive network

initial experimental results on impact of information loss/error on socio-cognitive networks with different topologies

Research Foci: modeling, predicting, and controlling the interdependencies between the different components of composite networks: formal theory of the semantics of multi-genre networks; information utility in composite networks; the impact of information loss in communication networks and information error in information networks on the structure and performance of socio-cognitive networks

Key Researchers: M. Dean, BBN J. Hendler, RPI P. Basu, BBN K. Carley, CMU B. Carterette, U Delaware N. Contractor, Northwestern W. Leland, BBN (IRC)


accurate analysis of the impact of communications and information networks on social and cognitive networks requires a mathematical approach that goes beyond the “bits” to address their interpretation in the presence of specific semantics

information utility is a function of human intent, quality of information (QoI), and the redundancy of the information (which in turn is affected by losses in the communications and information networks)

error creation and propagation is an emergent property of the structures of socio-cognitive networks and information networks as they use rapidly-evolving communications networks: altering these structures affects human-visible network information and resultant human behaviors


Project R3: Experimentation with Composite Networks

Key Products:

initial combined network, persistence, and validation requirements envisioned by Alliance researchers

roadmap for initial network and resource linkages (envisioned exemplars include IDVRN, MSRC, MNMI, CASEL, and WEL)

VPN operation of web services with NS CTA Facility

Initial Experimentation Plan, extending from individual information, communications, and social/cognitive network experimentation to integrated multi-genre experimentation

pilot experimental modules for initial testing

initial coordination and networking of experimental modules across multiple locations

coordination of training-based experimental modules with existing, or in-process, larger network simulation systems (to encompass simulation or emulation of information and communications networks)

Research Foci: joint design and conduct of geographically distributed research environments and experiments to validate network science models, methods, and theories; use of joint ontologies, metrics, and tools to explore the impact of research findings on Army challenges

Key Researchers: A. Leung, BBN J. P. Hancock, ArtisTech Inc. C. Partridge, BBN D. Williams, USC M. S. Poole, UIUC J. Srivastava, U Minnesota N. Contractor, Northwestern D. Sincoskie, University of Delaware C. Cotton, University of Delaware


coordinate networked linkage of internet, VPN, and IDVRN between IRC, ARCs, and ARL to facilitate distributed experimentation on testbed, computing elements, data sets, and instrumentation as defined by research needs

support levels of experimental interaction varying from batch-like tasking on assets that are unique, to piecewise incremental processing, to scaled real-time (especially in simulation), to real-time simulation and emulation with human users in the loop

create a basic package of communications tools to enable all willing CTA Centers, institutions, companies, and individual researchers to form a distributed interoperable infrastructure for heterogeneous environments supporting network science experimentation

achieve multi-genre experimentation and validation by integrating simulation and emulation systems that span information network modeling, envisioned communications networks, and warfighter training systems with interactive multiplayer virtual environments

2010-01-27 NS CTA Seminar 24

Non-CCRI Research: Information Networks Academic Research Center (INARC)

Director: Jiawei Han, UIUC Email: [email protected]

Government Lead: Lance Kaplan Email: [email protected]

Projects and Leads:

Project I1, Distributed and Real-time Data Fusion and Information Extraction: C. Aggarwal (IBM); T. Abdelzaher, UIUC (INARC)

Project I2, Scalable, Human-Centric Information Network Systems: X. Yan, UCSB (INARC)

Project I3, Knowledge Discovery in Information Networks: J. Han, UIUC (INARC)


Project I1: Distributed and Real-time Data Fusion and Information Extraction

Key Products:

bounded-error object representation and summarization algorithms for fusing multiple distributed streams with logical information network links

algorithms for fusing multiple sensor streams with information network linkages into a single representation

end-to-end latency analysis of multi-stream fusion

methods to use node centrality and correlation analysis on multi-modal sources to increase information gain

approximation methods for determining uncertainty in joint inference, with research experimentation for validation of quality and performance

algorithms for multi-modal information-value optimization subject to time and resource constraints

specific methods for multi-stream information fusion in militarily-relevant exemplars scenarios (such tracking and recognition of vehicles)

Research Foci: information network construction for multi-modal information: inferring the virtual links and ontologies among diverse information sources, fusing such information in order to build semantically relevant objects, and addressing the uncertainty inherent in the use of such data sources

Key Researchers: T. Abdelzaher, UIUC C. Aggarwal, IBM A. Bar-Noy, CUNY T. Höllerer, UCSB T. Huang, UIUC H. Ji, CUNY B. S. Manjunath, UCSB S. Papadimitriou, IBM D. Roth, UIUC A. Singh, UCSB


utilize logical linkages among widely distributed and diverse data sources in order to build a semantically meaningful information network for inference and knowledge discovery

unify inherently different data sources (including signal, human, and visual sources) to reduce the uncertainty of inference and information fusion

model and analyze the non-uniform uncertainty of these disparate sources in the context of the fusion process, and how it propagates into the extracted information


Project I2: Scalable, Human-Centric Information Network Systems

Key Products:

models for information fusion, considering QoI, using stochastic information dissemination, Bayesian probabilistic, and time-sensitive mathematical approaches

models of how to modulate visualizations to optimize performance given contextual & resource constraints

scalable graph-indexing mechanisms for information aggregation in complex networks

multi-model fusion methods for realization of QoI integration within information networks supporting communications and social/cognitive network needs

on-line analytic processing operators for graph aggregation and graph association

algorithms for path-based and reverse-path-based ranking of node similarity

visualization methods that exploit these techniques for situational awareness

Research Foci: organization and management of large heterogeneous information networks for scalable information search and efficient knowledge discovery in composite networks; design and research validation of human-centric, intuitive, scalable interfaces that automatically take into account the situation and cognitive states of users, as well as physical constraints

Key Researchers: X. Yan, UCSB C. Aggarwal, IBM T. Brown, CUNY J. Han, UIUC T. Höllerer, UCSB A. Kementsietsidis, IBM P. Pirolli, PARC A. Singh, UCSB M. Wang, IBM


design integrated data models and provenance metadata organization to support complex multi-network environments

exploit stochastic information dissemination for scalable knowledge discovery, initially using a Bayesian probabilistic model to approximate the data organization

create integrated algorithms for hierarchical exploration of both information and topological dimensions to extend on-line analytic processing (OLAP) to the context of large, heterogeneous networks

design a multidimensional OLAP framework for information network analysis exploiting multidimensional topic modeling techniques and new path-based similarity ranking of information objects

drive network visualization from scalable graph representations, user context, and platform characterizations


Project I3: Knowledge Discovery in Information Networks

Key Products:

techniques for pattern discovery in distributed and volatile information networks

real-time methods for spatiotemporal analysis in heterogeneous networks

dynamic query expansion methods based on entity and event co-reference

techniques for multidimensional text mining for information network analysis

bootstrapping methods to improve the robustness of estimating low-level proximity/similarity metrics as well as higher-level recommendations

research validation and performance enhancements of these methods

Research Foci: effective and scalable knowledge discovery in distributed and volatile information networks with heterogeneous nodes and links; knowledge discovery from integrating real-time data streams; the spatiotemporal dimensions of knowledge in networks

Key Researchers: J. Han, UIUC C. Aggarwal, IBM C. Faloutsos, CMU H. Ji, CUNY S. Papadimitriou, IBM D. Roth, UIUC X. Yan, UCSB


exploit information relationships and reduce redundancies to determine which data sources should be used and where enhanced communications quality will provide the most benefit, since not all information sources are always available in heterogeneous, distributed networks

discover frequent and dynamic patterns in volatile heterogeneous information networks using rank-based multidimensional clustering

use spatiotemporal information clustering to significantly improve the scalability and accuracy of analysis of information in complex, multilevel networks

exploit the redundancy of different data sources from heterogeneous networks (that provide direct or indirect associations between entities) to achieve robust knowledge discovery


Non-CCRI Research: Social/Cognitive Academic Research Center (SCNARC)

Director: Boleslaw Szymanski, RPI Email: [email protected]

Government Lead: Jeff Hansberger, ARL Email: [email protected]

Projects and Leads:

Project S1 – Networks in Organization: C.-Y. Lin, IBM (SCNARC)

Project S2 – Adversary Social Networks: Detection and Evolution: M. Magdon-Ismail, RPI (SCNARC)

Project S3 – The Cognitive Social Science of Net-Centric Interactions: W. Gray, RPI (SCNARC)

Project S4 – Community Formation and Dissolution in Social Networks: G. Korniss, RPI (SCNARC)


Project S1: Networks in Organizations

Key Products:

recommendations for designing an efficient infrastructure

novel study results and understanding of human networks, as well as further understanding of network-affected performance

studies and models of multi-channel networks of people

Research Foci: study the challenge in building an infrastructure for gathering and handling large-scale heterogeneous streams in social network research, within the context of information networks and communication networks

Key Researchers: C.-Y. Lin, IBM R. Konuru, IBM Z. Wen, IBM S. Papadimitriou, IBM S. Aral, NYU/MIT E. Brynjolfsson, MIT T. Brown, CUNY A. (Sandy) Pentland, MIT A.-L. Barabasi, NEU D. Lazer, NEU/Harvard A. Vespignani, Indiana J. Hendler, RPI B. Uzzi, Northwestern


model, measure and quantify the impact of dynamic informal organizational networks on the productivity and performance of individuals and teams; develop and apply methods to identify causal relationships between dynamic networks and productivity, performance and value

investigate the multi-channel networks between people

use data captured by SmallBlue on interactions between IBM employees


Project S2: Adversary Social Networks: Detection and Evolution

Key Products:

preliminary methods for hidden community detection, based on statistical communication patterns, establishing overlap as a defining property

preliminary large scale models of information flow through social networks via trusted links and communities

Research Foci: identify adversary networks, understand how they evolve and relate to each other; develop a theory of how community structure and inhomogeneities in social networks, together with trust relationships, affect the flow of information through the network through trusted and non-trusted communities

Key Researchers: M. Magdon-Ismail, RPI M. Goldberg, RPI; W. Wallace, RPI B. Szymanski, RPI S. Adali, RPI B. Uzzi, Northwestern Z. Toroczkai, ND N. Hawla, ND


use measurable interactions between members of the social networks as the lens for understanding communities and information flow

use real data, from multiple networks on multiple scales (Enron email corpus, IBM SmallBlue, LiveJournal, Twitter)

use an agent based model

the value of information changes depending on the path it takes through trusted and non-trusted communities

agents combine information from different sources

it is possible to discover community structure using statistical graph-theoretical analysis of the interaction data


Project S3: The Cognitive/Social Science of Net-Centric Interactions

Key Products:

a reliable and valid neuroscience measure of trust that can be used in more applied work with future net-centric systems

time parameters for the emergence of a “trust” appraisal that can be used in computational cognitive models to predict trust in complex technology-mediated human-human interactions

Argus-Army: a simulated task environment; repurposes an existing team simulation environment for research in cognitive social science constructs of net-centric interactions

a combination of software standards and software systems that can be applied by the developers and users of the net-centric systems developed by the four ARCs

Research Foci: apply the theories, behavioral and neuroscience data collection techniques, and modeling (computational and mathematical) approaches of the cognitive sciences to uncovering and modeling the cognitive mechanisms that underlie human-human, human-technology, and human-information social interactions, particularly when using net-centric technologies

Key Researchers: W. D. Gray, RPI M. J. Schoelles, RPI J. Mangels, CUNY


limits on cognitive resources or cognitive processing at the 300 to 3000 ms time affect social behavior social behaviors place demands on cognitive resources and processing that may limit basic information processing mechanisms

successful higher-level cognitive performance is rooted in the demands made on immediate interactive behavior at the 300 to 3000 ms level of cognitive processing and resource allocation


Project S4: Community Formation and Dissolution in Social Networks

Key Products:

individual-based models and theories for social dynamics applicable for community detection in various social networks

models, theories, and methods to efficiently ideologically disintegrate and dissolve communities in social graphs

Research Foci efficient strategies and trade-offs for attacking and disintegrating adversarial communities with hostile, extremist, and/or militant ideologies

Key Researchers: G. Korniss, RPI B. Szymanski, RPI C. Lim, RPI M. Magdon-Ismail, RPI A.-L. Barabasi, NEU T. Brown, CUNY


develop individual-based models to investigate spread of ideologies, social influence, and associated processes in large-scale social networks

perform a systematic comparative analysis of social engineering and influencing based on predictive models of social behavior based on social network interactions


Non-CCRI Research: Communications Networks Academic Research Center (CNARC)

Coordinator: Thomas F. La Porta, Penn State Email: [email protected]

Government Lead: Greg Cirincione, ARL Email: [email protected]

Projects and Leads:

Project C1: Modeling Data Delivery in Dynamic, Heterogeneous, Mobile Networks: A. Yener, Penn State; R. Govindan, USC

Project C2: Characterizing the Increase of QoI due to Networking Paradigms: T. F. La Porta, Penn State

Project C3: Achieving QoI Optimal Networking (Deferred): TBA


Project C1: Modeling Data Delivery in Dynamic Heterogeneous, Mobile Networks

Key Products:

a preliminary OICC framework including impact of realistic constraints identified to be crucial

a first instantiation of the models to derive expressions for at least the information carrying capacity of a given network

a multidimensional stochastic model of QoI

a general algorithm for finding the local optima for a class of stochastic QoI functions

characterization of the impact of interference dynamics on the instantaneous and intermittent connectivity of an integrated heterogeneous network

a fundamental mathematical relationship to capture the impact of provenance and confidentiality on QoI

Research Foci: Characterize the impact of communications networks, including data delivery characteristics, security properties and source selection, on the operational information content capacity of a tactical network

Key Researchers: A.Yener, PSU R. Govindan, USC A. Bar-Noy, CUNY P. Brass, CUNY G. Cao, PSU G. Kramer, USC B. Krishnamachari, USC S. Krishnamurty, UCR T. F. La Porta, PSU K. Levitt, UCD P. Mohapatra, UCD M. Neely, USC K. Psounis, USC R. Ramanathan, BBN N. Young, UCR Q. Zhao, UCD M. Srivatsa, IBM (INARC) T. Abdelzaher, UIUC (INARC)

Key Technical Ideas: Network data delivery characteristics and security properties will have a joint impact on the Quality of Information (QoI) delivered by a tactical network

Dynamics of communications networks, and underlying information and social networks will impact the amount of data that can be delivered with certain quality, thus impacting the OICC of the network.


Project C2: Characterizing the Increase of QoI due to Networking Paradigms

Key Products:

characterization of conditions within which interference management is practical given overhead of algorithms

quantification of attainable capacity gains in terms of OICC with the use of multi-radio nodes under limited feedback conditions

characterization of performance of interference management against that of distributed cooperative MIMO schemes, with emphasis on dynamic networks

algorithms for in-network storage that increase OICC by leveraging underlying social and information networks

bounds on caching performance given QoI requirements

characterization of achievable performance of idealized scheduling algorithms that have certain levels of knowledge about the future

practical scheduling algorithms that handle arbitrary traffic, channels, and mobility, considering tradeoffs of latency with respect to QoI

Research Foci: examine key network paradigms to determine if they improve QoI and thus increase OICC, and why

Key Researchers: T. F. La Porta, PSU A Bar-Noy, CUNY G. Cao, PSU J. J. Garcia-Luna-Aceves, UCSC B. Krishnamachari, USC S. Krishnamurthy, UCR M. Neely, USC K. Psounis, USC H. Sadjapour, UCSC A. Yener, PSU Q. Zhao, UCD


consideration of underlying information and social networks will allow smarter choices in resource allocation, thus greatly increasing OICC above those algorithms that consider only communication network characteristics

collaborative communications (to leverage intelligence and cooperation), in-network storage (to leverage underlying social and information networks), scheduling mechanisms (to leverage use of resources) are critical degrees of freedom in networks to improve QoI

incorporation of inherent signal overhead, heterogeneous nature of information flows, heterogeneity of nodes


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