GETTING CONNECTED: SOCIAL SCIENCE IN THE AGE OF NETWORKS CAPSTONE PRESENTATION

1. GETTING CONNECTED:SOCIAL SCIENCE IN THE AGE OF NETWORKSCAPSTONE PRESENTATION Presenters: David Easley, Jon Kleinberg, Kathleen O’Connor, Michael Macy, Dan HuttenlocherRest of the Team: John Abowd, Larry Blume, Geri Gay, Jeffrey Prince, David StrangTeam Postdocs: Mary Still, Ted Welser April 23, 2008

2. The Cornell Networks Team • From across Cornell: Arts & Sciences, CALS, CIS, ILR, Johnson School

3. What are Networks? Transportation Network

4. Social Networks with Data Collected by Hand Friendships in a 34-person karate club that split apart---Zachary, 1977 Nodes-people, Edges-friendships

5. Social Network Discovered from Traces of Online Data Email communication between 436 employees in HP Research Lab—Adamic and Adar, 2005

6. Social Science and Networks Trade flows between countries Structure and Power Blume, Easley, Kleinberg+Tardos, 2007 Krempel+Plumper, 2003

7. Cascades, the Spread of Rumors, the Reliability of Information Links between political blogs prior to 2004 election---Adamic+Glance, 2005

8. Networks are Everywhere The study of networks integrates ideas from the social sciences and computer science, as well as information science, statistics, biology, physics… The growth of the Internet has provided us with data that previously was difficult or impossible to obtain Cornell is a leader in this area

9. Networks and the ISS Encourage collaboration across disciplinary boundaries • Ongoing research between economists, sociologists, psychologists, and computer and information scientists Engage the Cornell community (faculty, graduate students and undergrads) in cutting-edge research • Post docs • Graduate students • New undergrad courses with large enrollment

10. Theme Project Activities • Workshops, seminars, reading groups • Educational initiatives • Funding and recruiting opportunities • New inter-disciplinary research directions

11. Conferences • Ran conferences on aspects of project theme • “Search and Diffusion in Social Networks” • “Symposium on Self-Organizing Online Communities” (co-sponsored by Microsoft) • Brought national leaders from academia and industry to campus • E.g., Ron Burt, Nosh Contractor, Paul Dimaggio, Matt Jackson, Michael Kearns, Bob Kraut, Peter Monge, Duncan Watts, Barry Wellman …

12. Educational Initiatives • New courses in all project areas, from introductory to graduate • Network material incorporated into existing courses • ECON, SOC, COMM, ILR, CIS, JGSM • “Networks”: new intro undergrad course • Cross-listed in ECON, SOC, CS, INFO • This spring: 280 students from 33 different majors

13. Networks (ECON/SOC/CS/INFO 204) A course on how the social, natural, and technological worlds are connected, and how the study of networks shed light on these connections. Topics include: how opinions, fads, and political movements spread through society; the robustness and fragility of food webs and financial markets; and the technology, economics, and politics of Web information and on-line communities. High-school dating (Bearman, Moody, & Stovel 2004) Corporate e-mail (Adamic and Adar, 2005)

14. Networks Class Blog

15. Recruiting and Funding • Networks activity on campus enhanced many other efforts • Recruiting directions related to networks in Sociology, Communication, and CIS • Large-Scale NSF funding • Cyberinfrastructure tools (2005-present) • New proposals being pursued by expanded version of project team

16. New Research Directions • Networks activity drew in many faculty beyond original project team • New research informed by perspectives from multiple areas • Next: two examples (out of many) • Social cognition and individual behavior • Social contagion and on-line communities

17. Social Networks Represent Relationships Among People People work collaboratively, share opinions, create new knowledge through their decisions to build a relationship (or not) How do people understand and navigate their social environments to gain resources they care about—ideas, opinions, social support, political allies, status, for example? (Stephen Sauer and Ted Welser)

18. Micro-Foundations of Social Networks • Systematic investigations into factors that influence people’s • Cognitions about their social networks • Intentions to create relationships (ties) • Efforts to create relationships • Goals • Understanding how networks evolve • A psychological account of the spread of influence and ideas in social systems

19. People and their Network Positions • Personality psychology perspective • People are endowed with traits that are heritable, unaffected by external influences, and stable across the life span • Links between people’s traits and their positions in their social networks (Klein, Lim, Saltz, & Mayer, 2004) • People who are high in neuroticism tend to be less central in their networks (advice and friendship)

20. A Novel Social Network on Second Life Mark (UK) James (beard) Jill (pink) Ben (glasses) Emma (penguin) Mary(brown pants) Scene from Second Life

21. Where We Are Going • How do people understand and navigate their social environments to gain resources they care about? • Develop interventions to teach people strategies to make them more effective • Better able to spot opportunities to build social capital • Better able to translate those opportunities into advantageous network positions • New forms of social engagement and interaction give us new (and improved?) ways of studying social cognition and social behavior

22. It certainly is a small world! That’s amazing you know my Uncle Charlie! A Chance Encounter in a Distant Land Leads to Small Talk…

23. Six Degrees of Separation Yet the world is small: 6˚ The planet is very large: 6.5b! How is this possible?

24. Adding to the Mystery… • Easy to explain if the social ties were random • But friendships tend to be highly clustered B A C

26. A few long-range ties Create “shortcuts” between otherwise distant nodes Solved by Watts & Strogatz • While preserving the clustering of a social network

27. The “Strength of Weak Ties” • Long-range ties tend to be relationally weak • Less frequent interaction • Lower trust and influence • But structurally strong • Access to new ideas and information • Accelerate the spread of disease

28. Weak Ties Are Key “Whatever is to be diffused can reach a larger number of people, and traverse a greater social distance, when passed through weak ties rather than strong.” -- Mark Granovetter, 1973 • A truism across the social & information sciences • But there are some intriguing anomalies...

29. The Chain-Letter Paradox* If most people are separated by only six degrees, why are chain letters hundreds of links long? Sequence of signatures on e-mail chain letter protesting the Iraq war, with 18,119 nodes, median depth is 288. *Liben-Nowell & Kleinberg 2008, “Tracing information flow on a global scale using Internet chain-letter data,” PNAS 105:4633-38.

30. The Problem of “Critical Mass” • If an epidemic can quickly leap continents and reach millions of people in a few days, why do social movements often spread spatially and incrementally prior to reaching a “take-off” point?

31. Why Are Communities Clustered? • A cluster is a dense “cloud” of mutual friends • How do these form? • Conventional wisdom: people join communities and then become mutual friends • Maybe it is actually the other way around: people join communities to be with mutual friends?

32. Social Cloud Formation • 875 LiveJournal (blogging) communities • Individuals one degree removed • Joining as a function of • Number of friends who are already members • Clustering among friends *Backstrom, Huttenlocher, Kleinberg, Lan, 2006. “Group Formation in Large Social Networks: Membership, Growth, & Evolution,” Proc. 12th ACM SIGKDD Intl. Conf. on Knowledge Discovery & Data Mining.

33. Number and clustering of friends A B C Time 1

34. Number and clustering of friends A B C Time 2

35. Number and clustering of friends A B C Time 3

36. Number and clustering of friends A B C Time 4

37. Number and clustering of friends A B C Time 5

38. Number and clustering of friends A B C Time 6

39. Number and clustering of friends A B C Time 7

40. Number and clustering of friends A B C Time 8

41. Why is Clustering Important? • Chain-letters and social movements seem to avoid taking “shortcuts” • It’s the mutual friends that seem to be key to growth of communities • If disease and information can take “shortcuts,” why can’t social contagions?

42. A Simple Explanation* • Social contagions differ from disease and information • Acquiring information is not the same thing as acting on it • The same information from two friends is redundant • The same advice from two friends is not • Credibility, legitimacy & utility of adoption usually increase with the number of prior adopters • Centola, D. and M. Macy. 2007. “Complex Contagions & the Weakness of Long Ties.” American Journal of Sociology 113:702-34

43. Maybe It’s Not Such a Small World After All? • Information and disease benefit from “weak ties” that create shortcuts • A single contact is sufficient for transmission • Clustering is therefore redundant • Social contagions benefit from clustering • “Redundancy” provides social reinforcement • Long-range ties inform but do not persuade

44. 1000000 100000 Timesteps 100000 Simple contagion that requires adoption by 1 neighbor 10000 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 (High Clustering) (No Clustering) Proportion of Random Ties Random ties promote the spread of information (lower is faster)

45. 10000000 Social contagion that requires adoption by 3 neighbors Phase transition in the social fabric: Contagion can no longer spread Social contagion that requires adoption by 2 neighbors 1000000 Timesteps 100000 Simple contagion that requires adoption by 1 neighbor 10000 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 (High Clustering) (No Clustering) Proportion of Random Ties But not the spread of social contagions

46. Small Worlds in a Bigger Picture • Social life is hard to observe • You can interview friends, but you cannot interview a friendship • Fleeting interaction • In private • Tedious to record over time, especially in large groups

47. Why This is Changing • Humans increasingly interact publicly online • Web pages, Facebook, blogs, wikis, games • Computer-mediated interaction leaves digital traces • New era of “connectionist” social science? • Interactions among people, not just variables • Networks, not just aggregates of individuals • Dynamics, not just comparative statics • Links the talents & tools of social, computer, and information scientists

48. Some closing observations • What’s next

49. Observations • What does it mean to do interdisciplinary work with a dozen faculty across such broad range of fields? • Sociology, economics, communications, social psychology, information science, computer science • More than joint projects across disciplinary boundaries – catalyst for research • Investigations deeply informed and motivated by research of members in other fields – but published in established (disciplinary) venues

50. Observations • Importance of residential year, with lead-in and follow-up years • Build deeper ties and understanding across disciplines through seminars, visitors, workshops, proposals, informal discussion • Exposure to both classical literature and current work in several areas • Educational initiatives at both graduate and undergraduate level also engage team members in broader understanding • Research that happened as a result