Noshir Contractor Jane S. & William J. White Professor of Behavioral Sciences Jane S. & William J. White Professor of Behavioral Sciences Professor of Ind. Engg & Mgmt Sciences, McCormick School of Engineering Professor of Communication Studies, School of Communication & Professor of Management & Organizations, Kellogg School of Management, Director, Science of Networks in Communities (SONIC) Research Laboratory Supported by NSF : OCI , IIS , IIS , SBE From Disasters to WoW: Enabling Knowledge Networks in the 21st century SONIC Advancing the Science of Networks in Communities

Each circle (node) represents one person in the data set. There are 2200 persons in this subcomponent of the social network. Circles with red borders denote women, and circles with blue borders denote men. The size of each circle is proportional to the person's body- mass index. The interior color of the circles indicates the person's obesity status: yellow denotes an obese person(body-mass index, 30) and green denotes a non-obese person. The colors of the ties between the nodes indicate the relationship between them: purple denotes a friendship or marital tie and orange denotes a familial tie.

Aphorisms about Networks n Social Networks: u u Its not what you know, its who you know. n n Cognitive Social Networks: u u Its not who you know, its who they think you know. n n Knowledge Networks: u u Its not who you know, its what they think you know. SONIC Advancing the Science of Networks in Communities

Cognitive Knowledge Networks SONIC Advancing the Science of Networks in Communities

Multidimensional Networks in Web 2.0 Multiple Types of Nodes and Multiple Types of Relationships SONIC Advancing the Science of Networks in Communities

WHY DO WE CREATE, MAINTAIN, DISSOLVE, AND RECONSTITUTE OUR COMMUNICATION AND KNOWLEDGE NETWORKS? SONIC Advancing the Science of Networks in Communities

Social Drivers: Why do we create and sustain networks? n Theories of self- interest n Theories of social and resource exchange n Theories of mutual interest and collective action n Theories of contagion n Theories of balance n Theories of homophily n Theories of proximity n Theories of co- evolution 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. SONIC Advancing the Science of Networks in Communities

A contextual meta-theory of social drivers for creating and sustaining communities SONIC Advancing the Science of Networks in Communities

Core Research Social Drivers for Creating & Sustaining Communities Business Applications PackEdge Community of Practice (P&G) Societal Justice Applications Cultural & Networks Assets In Immigrant Communities (Rockefeller Program on Culture & Creativity) Mapping Digital Media and Learning Networks (MacArthur Foundation) Entertainment Applications Virtual Worlds Exploratorium (NSF, Sony Online Entertainment, Linden Labs) Science Applications CI-Scope: Understanding & Enabling CI in Virtual Communities (NSF) CP2R: Collaboration for Preparedness, Response & Recovery (NSF) TSEEN: Tobacco Surveillance Evaluation & Epidemiology Network (NSF, NIH, CDC) Projects Investigating Social Drivers for Communities SONIC Advancing the Science of Networks in Communities

Contextualizing Goals of Communities Challenges of empirically testing, extending, and exploring theories about networks … until now SONIC Advancing the Science of Networks in Communities

Enter Semantic Web/Web 2.0 Its all about Relational Metadata n Technologies that capture communities relational meta-data (Pingback and trackback in interblog networks, blogrolls, data provenance) n Technologies to tag communities relational metadata (from Dublin Core taxonomies to folksonomies (wisdom of crowds) like u Tagging pictures (Flickr) u Social bookmarking (del.icio.us, LookupThis, BlinkList) u Social citations (CiteULike.org) u Social libraries (discogs.com, LibraryThing.com) u Social shopping (SwagRoll, Kaboodle, thethingsiwant.com) u Social networks (FOAF, XFN, MySpace, Facebook) n Technologies to manifest communities relational metadata (Tagclouds, Recommender systems, Rating/Reputation systems, ISIs HistCite, Network Visualization systems) SONIC Advancing the Science of Networks in Communities

Text Mining Web crawling Web of Science Citation CATPAC UBERLINK Digital Harvesting of Relational Metadata CI-KNOW Analyses and Visualizations SONIC Advancing the Science of Networks in Communities

CI-KNOW: Harvesting the online communitys relational meta-data INPUTS Cybercommunity Resources Cyberinfrastructure Use External Resources Generating a Multi- Dimensional network Network Analysis PROCESSES Network Maps Network Referrals OUTPUTS Network Diagnostics Users Profiles Documents Collaboration Tools Datasets Analysis Tools Bibliographic DBs Personal Websites Organizational Websites Project Websites Patent Databases Linking all data together 1.Algorithms to generate Network Referrals 2. Algorithms to create Network Maps 3. Algorithms to compute Network Diagnostics User activity logs related to cyberinfrastructure Downloading Presentations Using Tools to Analyze Datasets Using Chats, Forum SONIC Advancing the Science of Networks in Communities

INPUTS Cybercommunity Resources Cyberinfrastructure Use External Resources Generating a Multi- Dimensional network Network Analysis PROCESSES Network Maps Network Referrals OUTPUTS Network Diagnostics 1.Who to contact for what topic 2.What tools to use for what data 3.What dataset to analyze for what concepts 4.What papers to read for what keywords 1.What nodes are important for what relations 2.The amount of scanning, absorption, diffusion, robustness, vulnerability in a network CI-KNOW: Harvesting the online communitys relational meta-data SONIC Advancing the Science of Networks in Communities

Core Research Social Drivers for Creating & Sustaining Communities Business Applications PackEdge Community of Practice (P&G) Societal Justice Applications Cultural & Networks Assets In Immigrant Communities (Rockefeller Program on Culture & Creativity) Mapping Digital Media and Learning Networks (MacArthur Foundation) Entertainment Applications Virtual Worlds Exploratorium (NSF, Sony Online Entertainment, Linden Labs) Science Applications CI-Scope: Understanding & Enabling CI in Virtual Communities (NSF) CP2R: Collaboration for Preparedness, Response & Recovery (NSF) TSEEN: Tobacco Surveillance Evaluation & Epidemiology Network (NSF, NIH, CDC) Projects Investigating Social Drivers for Communities SONIC Advancing the Science of Networks in Communities

Hurricane Katrina 2005 Formed:Aug 23, 2005 Dissipated:Aug 31, 2005 Highest wind:175 mph Lowest press:902 mbar Damages:$81.2 Billion Fatalities:>1,836 Areas affected:Bahamas, South Florida, Cuba, Louisiana (especially Greater New Orleans), Mississippi, Alabama, Florida Panhandle, most of eastern North America Map source: 8/23 8/24 8/25 8/26 8/27 8/28 8/29 8/30 8/31 SONIC Advancing the Science of Networks in Communities

SITREP Content n Basic Format / Information 1. Situation (What, Where, and When) 2. Action in Progress 3. Action Planned 4. Probable Support Requirements and/or Support Available 5. Other items SONIC Advancing the Science of Networks in Communities

Typical SITREP *Colorado Division of Emergency Management SITUATION REPORT (Hurricane Katrina) August 30, 2005* *Event Type:* Hurricane Response *Situation:* On August 29, Hurricane Katrina hit the gulf coast east of New Orleans. It was considered a Category 5 Hurricane, which brings winds of over 155mph and storm surge of 18 feet above normal. Massive property damage has occurred and undetermined number of deaths and injuries. Colorado response to date include two deployments: - Two members from the Division of Emergency Management to the Louisiana EOC, departed on August 29. · · · *Weather Report:* Katrina is moving toward the north-northeast near 18 mph. A turn toward the northeast and a faster forward speed is expected during the next 24 hours. This motion should bring the cent · · · *Agencies Involved:* Colorado Department of Military and Veteran Affairs, Department of Local Affairs, Division of Emergency Management, Governor's Office.* * *Additional Assistance Requested:* Type III teams, consisting of Operations, Plans, and Logistics personnel (two individuals for each area). These teams could deploy to Alabama, Louisiana, and/or Mississippi. Teams will be at either working the State or Parish/County EOCs. · · · SONIC Advancing the Science of Networks in Communities

Human Coding Procedure n Using an HTML editor to mark entities (people, organizations, locations, concepts) u as bold and include a unique HTML tag u FEMA u FEMA SONIC Advancing the Science of Networks in Communities

Automatic Coding n D2K – The Data to Knowledge application environment is a rapid, flexible data mining and machine learning system n Automated processing is done through creating itineraries that combine processing modules into a workflow n Developed by the Automated Learning Group at NCSA SONIC Advancing the Science of Networks in Communities

Time Slice 1: 8/23 to 8/25/2005 ARC SAL FEMA Shelter TX KY AL LA NO Gov Bush FL Petroleum Network formed Early Florida is the Topic of the Conversation SONIC Advancing the Science of Networks in Communities

Time Slice 1 to 2 ARC SAL FEMA Shelter TX KY AL LA NO Gov Bush FL Power FP&L GA Military SONIC Advancing the Science of Networks in Communities

Time Slice 2: 8/26 to 8/27/2005 ARC SAL FEMA Shelter TX MS LA NO Gov Bush FL Power FP&L GA Military SONIC Advancing the Science of Networks in Communities

Time Slice 2 to 3 ARC SAL FEMA Shelter TX MS LA NO Gov Bush FL Power FP&L GA Military NC SONIC Advancing the Science of Networks in Communities

Time Slice 3: 8/28 to 8/29/2005 ARC FEMA Shelter TX MS LA NO Gov Bush FL Power FP&L NC Military GA SONIC Advancing the Science of Networks in Communities

Time Slice 3 to 4 ARC FEMA Shelter TX MS LA NO Gov Bush FL Power FP&L NC Military GA AL Power S & R National Guard AL SONIC Advancing the Science of Networks in Communities

Time Slice 4: 8/30 to 8/31/2005 ARC FEMA Shelter TX MS LA NO FL Power FP&L NC GA AL Power S & R National Guard AL SONIC Advancing the Science of Networks in Communities

Time Slice 4 to 5 ARC FEMA Shelter TX LA NO FL Power FP&L NC GA AL Power S & R National Guard MS AL SONIC Advancing the Science of Networks in Communities

Time Slice 5: 9/1 to 9/2/2005 ARC FEMA Shelter TX MS LA NO FL Power NC GA AL Power S & R National Guard AL SONIC Advancing the Science of Networks in Communities

Time Slice 5 to 6 ARC FEMA Shelter TX MS LA NO FL Power GA AL Power S & R National Guard AL SONIC Advancing the Science of Networks in Communities

Time Slice 6: 9/3 to 9/4/2005 ARC FEMA Shelter TX MS LA NO FL Outages GA AL Power Urban S & R National Guard AL S & R SONIC Advancing the Science of Networks in Communities

Change in Network Centrality Rankings American Red Cross starts in the 200s and moves to the teens FEMA starts in the 20s, moves to the teens, and ends in the 60s FEMA drops rank and American Red Cross moves up Crossover where American Red Cross becomes relatively more central than FEMA (Sep 1, 2005) SONIC Advancing the Science of Networks in Communities

Core Research Social Drivers for Creating & Sustaining Communities Business Applications PackEdge Community of Practice (P&G) Societal Justice Applications Cultural & Networks Assets In Immigrant Communities (Rockefeller Program on Culture & Creativity) Mapping Digital Media and Learning Networks (MacArthur Foundation) Entertainment Applications Virtual Worlds Exploratorium (NSF, Sony Online Entertainment, Linden Labs) Science Applications CI-Scope: Understanding & Enabling CI in Virtual Communities (NSF) CP2R: Collaboration for Preparedness, Response & Recovery (NSF) TSEEN: Tobacco Surveillance Evaluation & Epidemiology Network (NSF, NIH, CDC) Projects Investigating Social Drivers for Communities SONIC Advancing the Science of Networks in Communities

3D Strategy for Enhancing Knowledge Networks n Discovery: Effectively and efficiently foster network links from people to other people, knowledge, and artifacts (data sets/streams, analytic tools, visualization tools, documents, etc.) u If only NSF knows what NSF knows. n Diagnosis: Assess the health of internal and external networks - in terms of scanning, absorptive capacity, diffusion, robustness, and vulnerability to external environment n Design: Model or re-wire networks using social and organizational incentives (based on social network research) and network referral systems to enhance evolving and mature communities SONIC Advancing the Science of Networks in Communities

Discovery Problems in Knowledge Networks n IDC found Fortune 500 companies lose $31.5 billion annually due to rework and the inability to find information. n The Delphi Consulting Group found that: u u Only 12 percent of a typical company's knowledge is explicitly published. Remaining 88 percent is distributed knowledge, comprised of employees' personal knowledge. u u Up to 42 percent of knowledge professionals need to do their jobs comes from other people's brains - in the form of advice, opinions, judgment, or answers. More often than not, much of this exchange does not follow channels displayed in an organizational chart. SONIC Advancing the Science of Networks in Communities

Discovery Challenges n Who knows who? n Who knows what? n Who know who knows who? n Who knows who knows what? SONIC Advancing the Science of Networks in Communities

Goal of Discovery – IKNOW SONIC Advancing the Science of Networks in Communities

Diagnosis: Why Diagnose the Network? n Naturally occurring networks are not always efficient or fully functional u Gaps, isolates, lack or difficulty of connectivity n Network measures can be used to diagnose networks vital statistics SONIC Advancing the Science of Networks in Communities

Diagnosis Questions How capable at scanning external expertise? How capable at absorbing expertise from the external network to the internal network? How efficient at diffusing the external expertise within the internal network? How robust in a specific area of expertise against disruption? How vulnerable to being externally brokered? SONIC Advancing the Science of Networks in Communities

Strongest capacity to absorb

From Diagnosis to Design 1. Identifying which network links need to be re-wired optimize the collective power of the network. 2. Identifying the Individual, Organizational and Social Incentives – for members to want to re-wire. SONIC Advancing the Science of Networks in Communities

Designing CoPs as Small World Networks n Industries with small world network structures are more innovative! u Networks where people spend most of their time communicating with one another in a group (cluster) and spend some time communicating with others outside (short cuts) u Small world networks exhibit high levels of clustering and few shortcuts F Clusters engender trust and control, maximize capability for exploitation F Shortcuts engender unique combinations of network resources, maximize capacity for exploration SONIC Advancing the Science of Networks in Communities

Pre-wired PackEdge CoP Network

Re-wired PackEdge CoP Network

Wiring the PackEdge CoP Network for Success Increase the likelihood to give and get information to the right target and source respectively n Benefits for CoP u Increase absorptive capacity from 45.3% to 53.4% u Reduce number of steps for diffusion from 4.3 to 2.6 n Costs for CoP u Increase communication links of network leaders from 28 to 38 (~ 150 new links). u Increase criticality of network leaders from 26.7 % to 48.5% SONIC Advancing the Science of Networks in Communities

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) Kraft Product Design Teams Societal Justice Applications Cultural & Networks Assets In Immigrant Communities (Rockefeller Program on Culture & Creativity) Digital Media and Learning (MacArthur Foundation) Entertainment Applications World of Warcraft (NSF) Everquest (NSF, ARI, Sony Online Entertainment) Second Life (Linden Labs) INFORMS paper by Yun Huang et al Science Applications VOSS: Virtual Organizations as Socio- technical Systems (NSF) CP2R: Collaboration for Preparedness, Response & Recovery (NSF) INFORMS paper by Mengxiao Zhu et al TSEEN: Tobacco Surveillance Evaluation & Epidemiology Network (NSF, NIH, CDC) Projects Investigating Social Drivers for Communities SONIC Advancing the Science of Networks in Communities

SONIC Advancing the Science of Networks in Communities

SONIC Advancing the Science of Networks in Communities

SONIC Advancing the Science of Networks in Communities

Source: Rise of WoW SONIC Advancing the Science of Networks in Communities

Expertise/Information Retrieval Time One

Expertise/Information Retrieval Time Two

Expertise/Information Retrieval Time Three

Unraveling the Structural Signatures n Incentive for creating a WoW link with someone = (cost of creating a link) [Self-interest] (benefit of reciprocating) [Exchange] (benefit for being a friend of a friend) [Balance] [Balance] (benefit of connecting to an expert) (benefit of connecting to an expert) [Cognition] [Cognition] All coefficients significant at 0.05 level SONIC Advancing the Science of Networks in Communities

Tobacco Research: TobIG Demo Computational Nanotechnology: nanoHUB Demo Cyberinfrastructure: CI-Scope Demo Oncofertility: Onco-IKNOWTobIG DemonanoHUB DemoCI-Scope DemoOnco-IKNOW Design Examples: Mapping & Enabling Networks in … SONIC Advancing the Science of Networks in Communities

Summary n Research on the dynamics of networks is well poised to make a quantum intellectual leap by facilitating collaboration that leverages recent advances in: u Theories about the social motivations for creating, maintaining, dissolving and re-creating social network ties u Development of cyberinfrastructure/Web 2.0 provide the technological capability to capture relational metadata needed to more effectively understand (and enable) communities. u Exponential random graph modeling techniques to make theoretically grounded network recommendations that go beyond the Lovegety and SNIF SONIC Advancing the Science of Networks in Communities

SONIC Team members Zack Johnson Undergrad, SONIC Sanjeev Jha Doctoral candidate, SONIC Jinling Li Research Programmer, SONIC York Yao Research Programmer, SONIC Yun Huang Post-doc, SONIC SONIC Advancing the Science of Networks in Communities

Acknowledgements SONIC Advancing the Science of Networks in Communities