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User Interactions in Social Networks and their Implications. University of California at Santa Barbara Christo Wilson , Bryce Boe , Alessandra Sala , Krishna P. N. Puttaswamy , and Ben Zhao. Social Networks. Social Applications.

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University of california at santa barbara

User Interactions in

Social Networks

and their Implications

University of California at Santa Barbara

Christo Wilson, Bryce Boe, Alessandra Sala, Krishna P. N. Puttaswamy, and Ben Zhao


Social networks

Social Networks

University of California at Santa Barbara


Social applications

Social Applications

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


Social graphs and user interactions

Social Graphs and User Interactions

University of California at Santa Barbara

  • 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


  • Goals

    Goals

    University of California at Santa Barbara

    • 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


    Outline

    Outline

    Characterizing Facebook

    Analyzing User Interactions

    Interaction Graphs

    Effects on Social Applications

    University of California at Santa Barbara


    Crawling facebook for data

    Crawling Facebook for Data

    University of California at Santa Barbara

    • 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


    Crawling for data cont

    Crawling for Data, cont.

    University of California at Santa Barbara

    • 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


    High level graph statistics

    High Level Graph Statistics

    • Based on Facebook’s total size of 66 million users in early 2008

    • Represents ~50% of all users in the crawled regions

    • ~49% of links were crawlable

    • This provides a lower bound on the average number of in-network friends

    • Avg. social degree = ~77

    • Low average path length and high clustering coefficient indicate Facebook is small-world

    1. A. Mislove, M. Marcon, K. P. Gummadi, P. Druschel, and B. Bhattacharjee. Measurement and analysis of online social networks. In Proc. of IMC, October 2007.

    University of California at Santa Barbara


    Outline1

    Outline

    Characterizing Facebook

    Analyzing User Interactions

    Interaction Graphs

    Effects on Social Applications

    University of California at Santa Barbara


    Analyzing user interactions

    Analyzing User Interactions

    University of California at Santa Barbara

    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


    Distribution among friends

    Distribution Among Friends

    • Social degree does not accurately predict human behavior

    • Initial Question: Are social links valid indicators of real user interaction?

      • Answer: NO

    For 50% of users, 70% of interaction comes from 7% of friends.

    Almost nobody interacts with more than 50% of their friends!

    For 50% of users, 100% of interaction comes from 20% of friends.

    University of California at Santa Barbara


    Outline2

    Outline

    Characterizing Facebook

    Analyzing User Interactions

    Interaction Graphs

    Effects on Social Applications

    University of California at Santa Barbara


    A better model of social graphs

    A Better Model of Social Graphs

    University of California at Santa Barbara

    • 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


    Interaction graphs

    Interaction Graphs

    University of California at Santa Barbara

    • 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}


    Social vs interaction degree

    Social vs. Interaction Degree

    1:1 Degree Ratio

    Dunbar’s Number (150)

    99% of Facebook Users

    • Interaction graph prunes useless edges

    • Results agree with theoretical limits on human social cognition

    University of California at Santa Barbara


    Interaction graph analysis

    Interaction Graph Analysis

    Do Interaction Graphs maintain expected social network graph properties?

    • Interaction Graphs still have

      • Power-law scaling

      • Scale-free behavior

      • Small-world clustering

    • … But, exhibit less of these characteristics than the full social network

    University of California at Santa Barbara


    Outline3

    Outline

    Characterizing Facebook

    Analyzing User Interactions

    Interaction Graphs

    Effects on Social Applications

    University of California at Santa Barbara


    Social applications revisited

    Social Applications, Revisited

    University of California at Santa Barbara

    • 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


    Sybilguard

    Sybilguard

    University of California at Santa Barbara

    • 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


    Sybilguard algorithm

    Sybilguard Algorithm

    Step 1:

    Bootstrap the network.

    All users exchange signed keys.

    Key exchange implies that both parties are human and trustworthy.

    Step 2:

    Choose a verifier (A) and a suspect (B).

    A and B send out random walks of a certain length (2).

    Look for intersections.

    A knows B is not a Sybil because multiple paths intersect and they do so at different nodes.

    B

    A

    University of California at Santa Barbara


    Sybilguard algorithm cont

    Sybilguard Algorithm, cont.

    B

    A

    University of California at Santa Barbara


    Sybilguard caveats

    Sybilguard Caveats

    University of California at Santa Barbara

    • Bootstrapping requires human interaction

      • Evaluating Sybilguard on the social graph is overly optimistic because most friends never interact!

    • Better to evaluate using Interaction Graphs


    Expected impact

    Expected Impact

    Fewer of edges, lower clustering lead to reduced performance

    Why? Self-loops

    B

    A

    University of California at Santa Barbara


    Sybilguard on interaction graphs

    Sybilguard on Interaction Graphs

    • When evaluated under real world conditions, performance of social applications changes dramatically

    University of California at Santa Barbara


    Conclusion

    Conclusion

    University of California at Santa Barbara

    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!


    Questions

    Questions?

    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

    University of California at Santa Barbara


    Social networks1

    Social Networks

    University of California at Santa Barbara

    • 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


    High level graph statistics1

    High Level Graph Statistics

    • Based on Facebook’s total size of 66 million users in early 2008

    • Represents ~50% of all users in the crawled regions

    • ~49% of links were crawlable

    • This provides a lower bound on the average number of in-network friends

    • Avg. social degree = ~77

    • Clustering Coefficient measures strength of local cliques

    • Measured between zero (random graphs) and one (complete connectivity)

    • Social networks display power law degree distribution

    • Alpha is the curve of the power law

    • D is the fitting error

    1. A. Mislove, M. Marcon, K. P. Gummadi, P. Druschel, and B. Bhattacharjee. Measurement and analysis of online social networks. In Proc. of IMC, October 2007.

    University of California at Santa Barbara


    Social degree cdf

    Social Degree CDF

    University of California at Santa Barbara


    Nodes vs total interactions

    Nodes vs. Total Interactions

    Top 10% of most interactive users are responsible for 85% of total interactions

    • Social degree does not accurately predict human behavior

    • Interactions are highly skewed towards a small percent of the Facebook population

    Top 10% of most well connected users are responsible for 60% of total interactions

    University of California at Santa Barbara


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