1. 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 nosh@northwestern.edu Supported by NSF : OCI-0753047, IIS-0729505, IIS-0535214, SBE-0555115 Digital Traces: An Exploratorium for Understanding and Enabling Social Networks SONIC Advancing the Science of Networks in Communities

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

3. 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

4. F E D B C A -+ Novice Expert “Structural signatures” of MTML Theories of Self interestTheories of Exchange Theories of Collective Action Theories of Balance Theories of HomophilyTheories of Cognition SONIC Advancing the Science of Networks in Communities

5. Statistical “MRI” for Structural Signatures n n p*/ERGM: Exponential Random Graph Models n n Statistical “Macro-scope” to detect structural motifs in observed networks n n Move from exploratory to confirmatory network analysis to understand multi- theoretical multilevel motivations for why we create our social networks SONIC Advancing the Science of Networks in Communities

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

7. Core Research Social Drivers for Creating & Sustaining Communities Business Applications PackEdge Community of Practice (P&G) Kraft (Kraft) 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

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

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

10. 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, ISI’s HistCite, Network Visualization systems) SONIC Advancing the Science of Networks in Communities

11. Bios, titles & descriptions Personal Web sites Google search results Web of Science Citation CATPAC UBERLINK Digital Harvesting of Relational Metadata CI-KNOW Analyses and Visualizations SONIC Advancing the Science of Networks in Communities

12. 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

13. 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: http://hurricane.csc.noaa.gov/ 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

14. 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

15. Typical SITREP *Colorado Division of Emergency Management SITUATION REPORT 2005-6 (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

16. 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

17. 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

18. 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

19. 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

20. 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

21. 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

22. 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

23. 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

24. 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

25. 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

26. 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

27. 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

28. 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

29. 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

30. Empirical Illustration Co-evolution of knowledge networks and 21 st century organizational forms n NSF KDI Initiative 1999-04. PI: Noshir Contractor, University of Illinois. n Co-P.I.s: Bar, Fulk, Hollingshead, Monge (USC), Kunz, Levitt (Stanford), Carley (CMU), Wasserman (Indiana). n Three dozen industry partners (global, profit, non-profit): u Boeing, 3M, NASA, Fiat, U.S. Army, American Bar Association, European Union Project Team, Pew Internet Project, etc. SONIC Advancing the Science of Networks in Communities

31. Transactive Memory u Perception of Other’s Knowledge u Communication to Allocate Information Social Exchange - Retrieval by coworkers on other topics Public Goods / Transactive Memory – Allocation to the Intranet – Retrieval from the Intranet – Perceived Quality and Quantity of Contribution to the Intranet Inertia Components – Collaboration – Co-authorship – Communication Communication to Retrieve Information Proximity -Work in the same location SONIC Advancing the Science of Networks in Communities

32. 1. Social Communication0.144 2. Perception of Knowledge & Communication to Allocate0.995 3. Perception of Knowledge & Provision0.972 4. Perception of Knowledge, Social Exchange, & Social Communication0.851 5. Perception of Knowledge, Proximity, & Social Communication0.882 Multi-theoretical p*/ERGM Theoretical Predictors of CRI SONIC Advancing the Science of Networks in Communities

33. Core Research Social Drivers for Creating & Sustaining Communities Business Applications PackEdge Community of Practice (P&G) Design Teams (Kraft) 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 VOSS: 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

34. Friendship in the Real World n Transitivity u Know the friend of a friend (Newman 2003) n Theory of Homophily u Friends with the same age and gender (Steglich, Snijders, & West 2006) n Theory of Proximity u Friends live nearby (Verbrugge 1977, Clark & Ayers 1988) SONIC Advancing the Science of Networks in Communities

35. Friendship in Second Life n 6.4 million users and 8 million friendship from Apr. 2001 to Sep. 2007. Number of Accounts Number of Days Stayed in Second Life SONIC Advancing the Science of Networks in Communities

36. Small World 4/1/200111/4/2006 Yellow dot (471,466) indicates less than 16 friend relations b/w users registered on 7/15 and 7/10 2002

37. Friendship in Teen Grid n Teen Second Life u An international gathering place for teens 13-17 to make friends and to play, learn and create. n All active players in the second quarter in 2007 u 2,456 users and 21,232 friendship n Do Homophily and Proximity still apply? SONIC Advancing the Science of Networks in Communities

38. Hypotheses n H1: Adolescents’ online friendship ties are not random. n H2: Real-world geographic proximity is positively associated with online friendship formation (geographic proximity). n H3: Time spent online is positively associated with friendship formation (digital proximity). n H4: Adolescents who join the virtual world at similar times are more likely to form friendships than adolescents who join at very different times (temporal proximity). SONIC Advancing the Science of Networks in Communities

39. Hypotheses (cont.) n H5: Adolescents of similar age are more likely to form friendships than adolescents who are not. n H6: Adolescents’ online friendships tend to be balanced (friends with their friends). n H7: Higher-status individuals, such as those with more experience in-world and premium accounts (where premium users incur a monthly fee in exchange for enhanced privileges) are more likely to form friendships than lower-status individuals. SONIC Advancing the Science of Networks in Communities

40. Data Description 2,456 Users, 21,232 Friendship Links, Density = 0.0070 2,456 Users, 21,232 Friendship Links, Density = 0.0070 1,747 male and 709 female 1,747 male and 709 female Mean age 15.52 (min 12.31, max 59.37) Mean age 15.52 (min 12.31, max 59.37) 2,267 basic accounts, 95 premium, 11 non revenue 2,267 basic accounts, 95 premium, 11 non revenue From 1086 cities in 48 countries From 1086 cities in 48 countries – 1961 users in the U.S., 166 UK, 117 Canada, 34 Australia, 26 Denmark, 23 Netherlands, 14 France, 10 Sweden, 9 Norway, 9 Belgium, 6 Mexico, 4 Spain, and etc. SONIC Advancing the Science of Networks in Communities

41. Female Male Size indicates age SONIC Advancing the Science of Networks in Communities

42. p*/Exponential Random Graph Models n Analyze network data with interdependencies – endogenous correlation among the relations n p*/ERGMs are a class of stochastic models: u g(y) is a vector of network statistics such as network structural measures and node attributes u θ is a vector of coefficients. Frank & Strauss, 1986; Pattison & Wasserman, 1999; Robins, Pattison, & Wasserman, 1999; Wasserman & Pattison, 1996; Hunter, 2007; Robins, Snijders, Wang, Handcock, & Pattison, 2007 Frank & Strauss, 1986; Pattison & Wasserman, 1999; Robins, Pattison, & Wasserman, 1999; Wasserman & Pattison, 1996; Hunter, 2007; Robins, Snijders, Wang, Handcock, & Pattison, 2007 SONIC Advancing the Science of Networks in Communities

43. Estimation Results Friendship Network Edges-8.845 (.015)*** Main Account=20.079 (.021)***Status (Premium accounts) Main Account=80.634 (.062)***Status (Non revenue accounts) Main log(Usage)=30.878 (.010)***Digital proximity Main log(Usage)=42.056 (.004)***Digital proximity Main log(Usage)=52.702 (.007)***Digital proximity Match City2.433 (.119)***Geographic proximity Difference Age-0.003 (.001) + Age Homophily Difference Registration Date -0.006 (.00002)***Temporal proximity GWESP(0.25)0.880 (.011)***Balance Signif. codes: 0 < *** < 0.001 < ** < 0.01 < * < 0.05 < + < 0.1 SONIC Advancing the Science of Networks in Communities

44. Hypotheses Tested H1: Friendship ties are not random. H1: Friendship ties are not random. H2: Geographic proximity is positively associated with friendship formation. H2: Geographic proximity is positively associated with friendship formation. H3: Digital proximity (time spent online) is positively associated with friendship formation. H3: Digital proximity (time spent online) is positively associated with friendship formation. H4: Temporal proximity (joining at similar times) is positively associated with friendship formation. H4: Temporal proximity (joining at similar times) is positively associated with friendship formation. H5: Age homophily are more likely to form friendships. (not very strong) H5: Age homophily are more likely to form friendships. (not very strong) H6: Friendships tend to be balanced. H6: Friendships tend to be balanced. H7: Higher-status individuals are more likely to form friendships. H7: Higher-status individuals are more likely to form friendships. SONIC Advancing the Science of Networks in Communities

45. Conclusion n Anonymous virtual world u No age, gender, and distance n Same friendship characteristics u Transitivity, age and gender homophily, proximity, and small world hypotheses are all supported n A virtual world is an extension of our real life? SONIC Advancing the Science of Networks in Communities

46. Core Research Social Drivers for Creating & Sustaining Communities Business Applications PackEdge Community of Practice (P&G) Kraft Design Teams (Kraft) 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 VOSS: 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

47. 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

48. 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

49. Acknowledgements SONIC Advancing the Science of Networks in Communities