Open Grid Forum workshop on Social Networks, Semantic Grids and Web

Noshir Contractor Professor, Departments of Speech Communication & Psychology Director, Age of Networks Initiative, Center for Advanced Study Director, Science of Networks in Communities - National Center for Supercomputing Applications University of Illinois at Urbana-Champaign [email_address] From Disasters to WoW How the Science of Networks can inform – and be informed by – the Grid and Web 2.0

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Key Takeaways ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Aphorisms about Networks ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Source: Newsweek, December 2000 Cognitive Knowledge Networks

Human Agent to Human Agent Communication Retrieving from knowledge repository Publishing to knowledge repository Non Human Agent to Non Human Agent Communication Non Human Agent (webbots, avatars, databases, “ push” technologies) To Human Agent INTERACTION NETWORKS Source: Contractor, 2001

Human Agent’s Perception of What Another Human Agent Knows Non Human Agent’s Perception of Resources in a Non Human Agent Non Human Agent’s Perception of what a Human Agent knows Human Agent’s Perception of Provision of Resources in a Non Human Agent COGNITIVE KNOWLEDGE NETWORKS * * … Why Tivo thinks I am gay and Amazon thinks I am pregnant ….

Human to Human Interactions and Perceptions Human to Non Human Interactions and Perceptions Non Human to Human Interactions and Perceptions Non Human to Non Human Interactions and Perceptions Non Human Agent Y Non Human Agent X Human C Human B Human A Non Human Agent Y Non Human Agent X Human C Human B Human A

WHY DO WE CREATE, MAINTAIN, DISSOLVE, AND RECONSTITUTE OUR COMMUNICATION AND KNOWLEDGE NETWORKS?

Social Drivers: Why do we create and sustain networks? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Sources: Contractor, N. S., Wasserman, S. & Faust, K. (2006). Testing multi-theoretical multilevel hypotheses about organizational networks: An analytic framework and empirical example. Academy of Management Review . Monge, P. R. & Contractor, N. S. (2003). Theories of Communication Networks . New York: Oxford University Press.

“ Structural signatures” of Social Drivers Theories of Self interest Theories of Exchange Theories of Collective Action Theories of Balance Theories of Homophily Theories of Cognition F E D B C A - + Novice Expert

Co-evolution of knowledge networks and 21 st century organizational forms ,[object Object],[object Object],[object Object],[object Object]

[object Object],[object Object],[object Object],Social Exchange - Retrieval by coworkers on other topics ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Motivations for search In Knowledge Nets Proximity -Work in the same location

1. Social Communication 0.144 2. Perception of Knowledge & Communication to Allocate 0.995 3. Perception of Knowledge & Provision 0.972 4. Perception of Knowledge, Social Exchange, & Social Communication 0.851 5. Perception of Knowledge, Proximity, & Social Communication 0.882 Motivation for Search in Knowledge Networks Using Exponential Random Graph Modeling Techniques

A contextual “meta-theory” of social drivers for creating and sustaining networks

Core Research Social Drivers for Creating & Sustaining Communities Business Applications PackEdge Community of Practice (P&G) Vodafone-Ericsson “Club” for virtual supply chain management (Vodafone) Societal Justice Applications Cultural & Networks Assets In Immigrant Communities (Rockefeller Program on Culture & Creativity) Economic Resilience NGO Community (Rockefeller Program on Working Communities) Entertainment Applications World of Warcraft (NSF) Everquest (NSF, Sony Online Entertainment) Science Applications CLEANER: Collaborative Large Engineering & Analysis Network for Environmental Research (NSF) CP2R: Collaboration for Preparedness, Response & Recovery (NSF) TSEEN: Tobacco Surveillance Evaluation & Epidemiology Network (NSF, NIH, CDC) Projects Investigating Social Drivers for Search/Diffusion in Networks

Contextualizing Goals of Communities Challenges of empirically testing, extending, and exploring theories about networks … until now

Enter: Cyberinfrastructure & Web 2.0

Components of Cyberinfrastructure ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Science and Engineering Cyberinfrastructures

Multidimensional Networks in CI (Cyberinfrastructure) Multiple Types of Nodes and Multiple Types of Relationships

Its all about “Relational Metadata” ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Bios, titles & descriptions Personal Web sites Google search results Web of Science Citation Digital Harvesting of Relational Metadata CI-KNOW Analyses and Visualizations http://iknowinc.com/iknow/sb_digital_forum/www/iknow.cgi CATPAC UBERLINK NETDRAW

Core Research Social Drivers for Creating & Sustaining Communities Business Applications PackEdge Community of Practice (P&G) Vodafone-Ericsson “Club” for virtual supply chain management (Vodafone) Societal Justice Applications Cultural & Networks Assets In Immigrant Communities (Rockefeller Program on Culture & Creativity) Economic Resilience NGO Community (Rockefeller Program on Working Communities) Entertainment Applications World of Warcraft (NSF) Everquest (NSF, Sony Online Entertainment) Science Applications CLEANER: Collaborative Large Engineering & Analysis Network for Environmental Research (NSF) CP2R: Collaboration for Preparedness, Response & Recovery (NSF) TSEEN: Tobacco Surveillance Evaluation & Epidemiology Network (NSF, NIH, CDC) Projects Investigating Social Drivers for Communities

CLEANER Grand Challenge ,[object Object],[object Object],[object Object]

Enabling Environmental Engineering Communities with Cyberinfrastructure ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Users can simultaneously view and discuss data and analyses

CLEANER Community: A multidimensional network NCSA David Streamflow Analyst Mary CHATS WITH USES Hydrologic Information System Status Report C-U County Hydrologic Dataset AFFILIATED WITH watersheds EXPERT IN River Monitoring Systems Contains as Keyword HUDSON RIVER Hydro DATA DOWNLOADS AUTHOR OF Searches for Keyword Annie

CI-KNOW: Harvesting the online community’s relational meta-data INPUTS Cybercommunity Resources Cyberinfrastructure Use External Resources PROCESSES Network Maps Network Referrals OUTPUTS Network Diagnostics Generating a Multi- Dimensional network Network Analysis Users’ Profiles Documents Collaboration Tools Datasets Analysis Tools Bibliographic DBs Personal Websites Organizational Websites Project Websites Patent Databases Linking all data together ,[object Object],[object Object],[object Object],User activity logs related to cyberinfrastructure Downloading Presentations Using Tools to Analyze Datasets Using Chats, Forum

CI-KNOW: Harvesting the online community’s relational meta-data INPUTS Cybercommunity Resources Cyberinfrastructure Use External Resources PROCESSES Network Maps Network Referrals OUTPUTS Network Diagnostics Generating a Multi- Dimensional network Network Analysis ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Cybercommunity Resources David C. writes a message on a CLEANER forum about hydrological monitoring systems. Documents Collaboration Tools Data sharing Analysis Tools CI-KNOW Portlet

Cybercommunity Resources Documents Collaboration Tools Data sharing Analysis Tools CI-KNOW Portlet

Recommending People David C. retrieves hydrological datasets on C-U County rivers CI-KNOW Portlet

[object Object],[object Object],[object Object],[object Object],[object Object],CI-KNOW: Cyberinfrastructure Knowledge Networks on the Web A Recommender System for Locating Resources in a Knowledge Network ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],What is a Knowledge Network? CI-KNOW Team Noshir Contractor, Hank Green, York Yao Steven Harper, Nat Bulkley, Andy Don Abstract. A Knowledge Network is a multi-dimensional network created from the interactions and interconnections among the people, documents, data, analytic tools, and interactive collaboration spaces (like forums and wikis) associated with a collaborative environment. CI-KNOW is a suite of software tools that leverages automated data collection and social network theories, analysis techniques and algorithms to infer an individual’s interests and expertise based on their interactions and activities within a Knowledge Network. Recent developments in social network theories and methods provide the backbone for a modular system that creates recommendations from relational metadata. The CI-KNOW Recommender mines the Knowledge Network associated with communities’ use of cyberinfrastructure tools and uses relational metadata to record connections among entities in the Knowledge Network. A network navigation portlet allows users to locate colleagues, documents, data or analytic tools in the network and to explore their networks through a visual, step-wise process. An internal auditing portlet offers administrators diagnostics to assess the growth and health of the entire Knowledge Network. The CI-KNOW recommender system is integrated with the CLEANER portal, created by the Environmental Cyberinfrastructure Demonstration Project, which supports the activities of environmental science communities (CLEANER and CUAHSI) under the umbrella of the WATERS network ( http://cleaner.ncsa.uiuc.edu ). It is also integrated with the ToBIG portal, created by the SONIC group to support the activities of tobacco researchers and policy makers ( http://tobig.ncsa.uiuc.edu ). CI-KNOW development is supported primarily by grants from the National Science Foundation and the National Cancer Institute and the National Institutes of Health. When individuals log in and uses a collaborative environment, their behavior is logged within a unique session that is linked to their user ID. Each session stores use logs as Resource Description Framework (RDF) triples that can be called ‘metadata’ Metadata summarizes objects (whether users or items), locates objects in geographical space, indexes content, stores and defines network ties among objects, and records usage and access information. A triple is information stored in the form: <subject><predicate><object> For example : <user 1><is author of><forum post 1> Predicates form the structural linkages between items in the knowledge network . CI-KNOW harvests structural metadata to record connections among entities in the knowledge network ,[object Object],[object Object],[object Object],[object Object],Funding for CI-KNOW development comes from NSF grants BES-0414259, BES-0533513, IIS-0535214, SBE-0555115, and SCI-0525308 and Office of Naval Research grant N00014-04-1-0437. Automated Metadata Collection CI-KNOW Recommender System Tools: Current and in Development The CI-KNOW portlet window “returns” a folded list of recommendations to users while they are working inside the portal. The recommendation itself is an active link to the contact information for the user or to the particular forum post, document, or data that is recommended. Clicking on the “Why” button in the portlet provides more detail about why a user has been given that particular recommendation and provides a text description and a network visualization of the path between the user, the search term and the recommendation. ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Knowledge Networks in CI (Cyberinfrastructure) CI-KNOW Metadata Taxonomies ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],CI-KNOW Technical Specifications CI-KNOW tools are being developed as independent web applications that can appear as portlets within a portal or collaborative environment. An intuitive user-interface enable members of the community to use the network referral system. To make the system sustainable, CI-KNOW continuously retrieves data from the community, processes these data, and uses internal network referral algorithms to update its network referrals for use within the community. The network navigation tools allow users to locate themselves or their colleagues, documents or workspaces in the knowledge network and to explore their networks through a visual, step-wise process. Users will have the option to explore these networks in an optimal ‘topological’ space, or to have the networks connected to geographical maps through an approach similar to the one used by Google Maps A Hybrid, Modular Recommender System ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],User Modified Rather than providing traditional search results (such as documents that contain a specific keyword), users receive recommendations to entities (datasets, documents, people) that are associated through transactions, interactions, and interconnections with items that contain the keyword. ,[object Object],[object Object],[object Object],How CI-KNOW Searching Works Internal Modifications CI-KNOW Tools in Development Concerns ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Instantiations of CI-KNOW Waters Network Portal CyberIntegrator ToBIG Portal The ‘Tell Me Why’ page presents information about the shortest path between the user, the search term and the recommendation. The ‘Tell Me Why’ visualization shows the shortest path between the user, the search term and the recommendation. With one click, the user can show all the shortest paths, hide node labels, and access more tools for Knowledge Network exploration

3D Strategy for Enhancing CoP Networks ,[object Object],[object Object],[object Object]

Mapping Flows in the PackEdge CoP Network

PackEdge CoP Vital Statistics ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

“ Pre-wired” PackEdge CoP Network

“ Re-wired” PackEdge CoP Network

Wiring the PackEdge CoP Network for Success ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Core Research Social Drivers for Creating & Sustaining Communities Business Applications PackEdge Community of Practice (P&G) Vodafone-Ericsson “Club” for virtual supply chain management (Vodafone) Societal Justice Applications Cultural & Networks Assets In Immigrant Communities (Rockefeller Program on Culture & Creativity) Economic Resilience NGO Community (Rockefeller Program on Working Communities) Entertainment Applications World of Warcraft (NSF) Everquest (NSF, Sony Online Entertainment) Science Applications CLEANER: Collaborative Large Engineering & Analysis Network for Environmental Research (NSF) : Collaboration for Preparedness, Response & Recovery (NSF) TSEEN: Tobacco Surveillance Evaluation & Epidemiology Network (NSF, NIH, CDC) Projects Investigating Social Drivers for Communities

WoW: Massively Multiplayer Online Role Playing Game

Source: http://www.mmogchart.com/ Rise of WoW

Goals of WoW Community ,[object Object],[object Object]

Mapping Goals to Theories: WoW Gaming Community

Data Collection ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Information Retrieval - Time One

Information Retrieval - Time Two

Information Retrieval -Time Three

Density of Communication Ties Decreases over Time

Unraveling the “Structural Signatures” ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],All coefficients significant at 0.05 level

Summary ,[object Object],[object Object],[object Object],[object Object]

SONIC Research Team Members Steven Harper Postdoctoral Research Associate NCSA, UIUC Hank Green Research Scientist NCSA, UIUC Chunke Su Graduate Research Assistant Speech Communication, UIUC Andy Don Research Programmer NCSA, UIUC Annie Wang Graduate Research Assistant Speech Communication, UIUC Mengxiao Zhu Graduate Research Assistant Speech Communication, UIUC Nat Bulkley Postdoctoral Research Associate NCSA, UIUC York Yao Research Programmer NCSA, UIUC Diana Jimeno-Ingrum Graduate Research Assistant Labor & Industrial Relations, UIUC

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