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University of California at Santa Barbara Christo Wilson, Bryce Boe, Alessandra Sala, Krishna P. N. Puttaswamy, and Ben

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University of California at Santa Barbara Christo Wilson, Bryce Boe, Alessandra Sala, Krishna P. N. Puttaswamy, and Ben

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    1. University of California at Santa Barbara Christo Wilson, Bryce Boe, Alessandra Sala, Krishna P. N. Puttaswamy, and Ben Zhao

    2. Social Networks 4/2/2009 University of California at Santa Barbara 2

    3. Social Applications 4/2/2009 University of California at Santa Barbara Enables new ways to solve problems for distributed systems Social web search Social bookmarking Social marketplaces Collaborative spam filtering (RE: Reliable Email) How popular are social applications? Facebook Platform – 50,000 applications Popular ones have >10 million users each 3

    4. 4/2/2009 Social Graphs and User Interactions Social applications rely on Social graph topology User interactions Currently, social applications evaluated just using social graph Assume all social links are equally important/interactive Is this true in reality? Milgram’s familiar stranger Connections for ‘status’ rather than ‘friendship’ Incorrect assumptions lead to faulty application design and evaluation University of California at Santa Barbara 4

    5. Goals 4/2/2009 University of California at Santa Barbara 5 Question: Are social links valid indicators of real user interaction? First large scale study of Facebook 10 million users (15% of total users) / 24 million interactions Use data to show highly skewed distribution of interactions <1% of people on Facebook talk to >50% of their friends Propose new model for social graphs that includes interaction information Interaction Graph Reevaluate existing social application using new model In some cases, break entirely

    6. Characterizing Facebook Analyzing User Interactions Interaction Graphs Effects on Social Applications Outline 4/2/2009 University of California at Santa Barbara 6

    7. Crawling Facebook for Data 4/2/2009 University of California at Santa Barbara 7 Facebook is the most popular social network Crawling social networks is difficult Too large to crawl completely, must be sampled Privacy settings may prevent crawling Thankfully, Facebook is divided into ‘networks’ Represent geographic regions, schools, companies Regional networks are not authenticated

    8. Crawling for Data, cont. Crawled Facebook regional networks 22 largest networks: London, Australia, New York, etc Timeframe: March – May 2008 Start with 50 random ‘seed’ users, perform BFS search Data recorded for each user: Friends list History of wall posts and photo comments Collectively referred to as interactions Most popular publicly accessible Facebook applications 4/2/2009 University of California at Santa Barbara 8

    9. High Level Graph Statistics 4/2/2009 University of California at Santa Barbara 9

    10. Characterizing Facebook Analyzing User Interactions Interaction Graphs Effects on Social Applications Outline 4/2/2009 University of California at Santa Barbara 10

    11. Analyzing User Interactions Having established that Facebook has the expected social graph properties… Question: Are social links valid indicators of real user interaction? Examine distribution of interactions among friends 4/2/2009 University of California at Santa Barbara 11

    12. Distribution Among Friends 4/2/2009 University of California at Santa Barbara 12

    13. Characterizing Facebook Analyzing User Interactions Interaction Graphs Effects on Social Applications Outline 4/2/2009 University of California at Santa Barbara 13

    14. A Better Model of Social Graphs 4/2/2009 University of California at Santa Barbara 14 Answer to our initial question: Not all social links are created equal Implication: can not be used to evaluate social applications What is the right way to model social networks? More accurately approximate reality by taking user interactivity into account Interaction Graphs Chun et. al. IMC 2008

    15. Interaction Graphs Definition: a social graph parameterized by… n : minimum number of interactions per edge t : some window of time for interactions n = 1 and t = {2004 to the present} 4/2/2009 University of California at Santa Barbara 15

    16. Social vs. Interaction Degree 4/2/2009 University of California at Santa Barbara 16

    17. Interaction Graph Analysis 4/2/2009 University of California at Santa Barbara 17 Do Interaction Graphs maintain expected social network graph properties?

    18. Characterizing Facebook Analyzing User Interactions Interaction Graphs Effects on Social Applications Outline 4/2/2009 University of California at Santa Barbara 18

    19. Social Applications, Revisited 4/2/2009 University of California at Santa Barbara 19 Recap: Need a better model to evaluate social applications Interaction Graphs augment social graphs with interaction information How do these changes effect social applications? Sybilguard Analysis of Reliable Email in the paper

    20. Sybilguard 4/2/2009 University of California at Santa Barbara 20 Sybilguard is a system for detecting Sybil nodes in social graphs Why do we care about detecting Sybils? Social network based games: Social marketplaces: How Sybilguard works Key insight: few edges between Sybils and legitimate users (attack edges) Use persistent routing tables and random walks to detect attack edges

    21. Sybilguard Algorithm 4/2/2009 University of California at Santa Barbara 21 Step 1: Bootstrap the network. All users exchange signed keys. Key exchange implies that both parties are human and trustworthy.

    22. Sybilguard Algorithm, cont. 4/2/2009 University of California at Santa Barbara 22

    23. Sybilguard Caveats 4/2/2009 University of California at Santa Barbara 23 Bootstrapping requires human interaction Evaluating Sybilguard on the social graph is overly optimistic because most friends never interact! Better to evaluate using Interaction Graphs

    24. Expected Impact 4/2/2009 University of California at Santa Barbara 24 Fewer of edges, lower clustering lead to reduced performance Why? Self-loops

    25. Sybilguard on Interaction Graphs 4/2/2009 University of California at Santa Barbara 25

    26. Conclusion 4/2/2009 University of California at Santa Barbara 26 First large scale analysis of Facebook Answer the question: Are social links valid indicators of real user interaction? Formulate new model of social networks: Interaction Graphs Demonstrate the effect of Interaction Graphs on social applications Final takeaway: when building social applications, use interaction graphs!

    27. Anonymized Facebook data (social graphs and interaction graphs) will be available for download soon at the Current Lab website! http://current.cs.ucsb.edu/facebook Questions? 4/2/2009 27 University of California at Santa Barbara

    28. 4/2/2009 Social Networks Social Networks are popular platforms for interaction, communication and collaboration > 110 million users 9th most trafficked site on the Internet > 170 million users #1 photo sharing site 4th most trafficked site on the Internet 114% user growth in 2008 > 800 thousand users 1,689% user growth in 2008 University of California at Santa Barbara 28

    29. High Level Graph Statistics 4/2/2009 University of California at Santa Barbara 29

    30. Social Degree CDF 4/2/2009 University of California at Santa Barbara 30

    31. Nodes vs. Total Interactions 4/2/2009 University of California at Santa Barbara 31

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