Noshir Contractor Professor, Departments of Speech Communication & Psychology

1. Analysis of Social Networks in Digital Government 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 nosh@uiuc.edu

2. Turn on power & set MODE with MODE button. You can confirm the MODE you chose as the red indicator blinks. • Lamp blinks when (someone with) a Lovegety for the opposite sex set under the same MODE as yours comes near. • FIND lamp blinks when (someone with) a Lovegety for the opposite sex set under different mode from yours comes near. May try the other MODES to “GET” tuned with (him/her) if you like.

3. Aphorisms about Networks • Social Networks: • Its not what you know, its who you know. • Cognitive Social Networks: • Its not who you know, its who they think you know. • Knowledge Networks: • Its not who you know, its what they think you know.

4. Cognitive Knowledge Networks Source: Newsweek, December 2000

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

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

7. 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

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

9. Monge, P. R. & Contractor, N. S. (2003). Theories of Communication Networks. New York: Oxford University Press.

10. Theories of self-interest Theories of social and resource exchange Theories of mutual interest and collective action Theories of contagion Theories of balance Theories of homophily Theories of proximity Theories of co-evolution Why do actors create, maintain, dissolve, and reconstitute network links? Sources: Monge, P. R. & Contractor, N. S. (2003). Theories of Communication Networks. New York: Oxford University Press. Contractor, N. S., Wasserman, S. & Faust, K. (in press). Testing multi-theoretical multilevel hypotheses about organizational networks: An analytic framework and empirical example. Academy of Management Review.

11. Projects on Enabling Networks • Networks to enable Digital Government Communities of Practice (CoP), NSF, NIH • Environment • Public Health • Emergency Response • Communities of Practice Networks, Procter &Gamble • Transnational Immigrant Networks, Rockefeller Foundation • Economic Justice/Resilience Networks, Rockefeller Foundation • Food Safety Networks, UIUC Cross-Campus Initiative & John Deere • Global Supply Chain Networks, Vodafone

12. Science and Engineering Cyberinfrastructures

13. Geosciences Cyberinfrastructures SEEK: The Science Environment for Ecological Knowledge

14. Multidimensional CoP Networks Multiple Types of Nodes and Multiple Types of Relationships

15. Cyberinfrastructure for CoP Networks • Environment: Collaborative for Large-scale Engineering Analysis Network for Environmental Research (CLEANER) • Public Health: Tobacco Surveillance, Epidemiology, and Evaluation Network (TSEEN)

19. Demo of multidimensional network

20. Cyberinfrastructure for CoP Networks • Environment: Collaborative for Large-scale Engineering Analysis Network for Environmental Research (CLEANER) • Public Health: Tobacco Surveillance, Epidemiology, and Evaluation Network (TSEEN)

21. Tobacco Surveillance, Epidemiology, and Evaluation Network (TSEEN) • National Cancer Institute • Center for Disease Control’s National Center for Health Statistics (NCHS), • Center for Disease Control’s Office of Smoking and Health (OSHO, • Agency for Healthcare Research and Quality (AHRQ), • National Library of Medicine (NLM) and • Non-government agencies such as the American Legacy Foundation.

22. TSEEN Network Referral System • Low-tar cigarettes cause more cancer than regular cigarettes … • A pressing need for systems that will help the TSEEN members effectively connect with other individuals, data sets, analytic tools, instruments, sensors, documents, related to key concepts and issues

23. 3D Strategy for Enhancing CoP Networks • Discovery: Effectively and efficiently foster network links from people to other people, knowledge, and artifacts (data sets/streams, analytic tools, visualization tools, documents, etc.). “If only we knew what we knew.” • Diagnosis: Assess the “health” of CoP’s internal and external networks - in terms of scanning, absorptive capacity, diffusion, robustness, and vulnerability to external environment • Design or re-wire networks using social and organizational incentives (based on social network research) and network referral systems to enhance evolving and mature communities.

24. “Discovery” Problems in CoP Networks • IDC found Fortune 500 companies lose $31.5 billion annually due to rework and the inability to find information. • The Delphi Consulting Group found that: • Only 12 percent of a typical company's knowledge is explicitly published. Remaining 88 percent is ‘distributed knowledge’, comprised of employees' personal knowledge. • 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.

25. Discovery Challenges • Who knows who? • Who knows what? • Who know who knows who? • Who knows who knows what?

26. Goal of Discovery – “IKNOW” http://iknow.spcomm.uiuc.edu Use courtesy logins and passwords provided on the website

27. Why Diagnose the CoP Network? • Naturally occurring networks are not always efficient or fully functional • Gaps, isolates, lack or difficulty of connectivity • Network measures can be used to diagnose network’s vital statistics

28. CoP Network’s Vital Statistics • Scanning • Absorption • Diffusion • Robustness • Vulnerability

29. 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 robustin a specific area of expertise against disruption? • How vulnerable to being externally brokered?

30. Diagnosis - Scanning Scanning from many sources (such as countries) US … US NL Rest of Network US IT BE … US IT BE BE Country codes indicated in nodes Internal External

31. Diagnosis - Absorbent Star Absorbent star links external experts to internal network Absorbent Star … I4 Rest of Network E1 I6 I3 E2 I1 I5 E3 I2 Internal External

32. Diagnosis - Diffusion Internal cluster not connected to the rest of the internal network E1 E3 E2 I4 I3 Rest of Network I5 I1 I8 I4 Isolated Internal Pocket I6 I7 … … Internal External

33. Diagnosis - Robustness Internal network not robust to loss of I3 E3 E5 E4 E1 I2 I1 E2 I3 I6 I4 I5 … … Internal External

34. Diagnosis - Vulnerability Internal network vulnerable to external expert E1 I2 I1 I3 I7 I5 I6 I4 E1 I8 External Bridge Internal External

35. State of Network Technology Areas Functional Areas Better than most Comparable to most Inferior to most

36. From Diagnostics to Design • Individual, Organizational and Social Incentives – Multi-theoretical Multilevel (MTML) Model • Socio-Technical Network Referral Systems: Technologies and Statistical Methods

37. Summary • The Lovegety underscore 21st century aspirations for more effective networking. • Recent advances in cyberinfrastructure development provides the technological capability to more effectively leverage our CoP networks. • Recent advances in communication networks research provides important insights into the social and organizational motivations that explain how we leverage our CoP networks. • We are poised for the design, development, and deployment of large scale socio-technical CoP network referral systems as part of the next generation cyberinfrastructures.

38. Science of Networks in Communities • nosh@uiuc.edu • www.uiuc.edu/ph/www/nosh