University of California at Santa Barbara

1
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

2
Social Networks
3
Social Applications

• 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 gt10 million users each

4
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?
• Milgrams familiar stranger
• Connections for status rather than friendship
• Incorrect assumptions lead to faulty application
  design and evaluation

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Goals

• 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
• lt1 of people on Facebook talk to gt50 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

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Outline

• Characterizing Facebook
• Analyzing User Interactions
• Interaction Graphs
• Effects on Social Applications

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Crawling Facebook for Data

• 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

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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

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High Level Graph Statistics

• Based on Facebooks 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.
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Outline

• Characterizing Facebook
• Analyzing User Interactions
• Interaction Graphs
• Effects on Social Applications

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

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Distribution Among Friends

• Social degree does not accurately predict human
  behavior
• Initial Question Are social links valid
  indicators of real user interaction?
• Answer NO

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Outline

• Characterizing Facebook
• Analyzing User Interactions
• Interaction Graphs
• Effects on Social Applications

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A Better Model of Social Graphs

• 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

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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

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Social vs. Interaction Degree

• Interaction graph prunes useless edges
• Results agree with theoretical limits on human
  social cognition

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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

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Outline

• Characterizing Facebook
• Analyzing User Interactions
• Interaction Graphs
• Effects on Social Applications

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Social Applications, Revisited

• 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

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Sybilguard

• 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

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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.
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Sybilguard Algorithm, cont.
B
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Sybilguard Caveats

• Bootstrapping requires human interaction
• Evaluating Sybilguard on the social graph is
  overly optimistic because most friends never
  interact!
• Better to evaluate using Interaction Graphs

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Expected Impact

• Fewer of edges, lower clustering lead to reduced
  performance
• Why? Self-loops

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Sybilguard on Interaction Graphs

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

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Conclusion

• 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!

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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

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Social Networks

• Social Networks are popular platforms for
  interaction, communication and collaboration
• gt 110 million users
• 9th most trafficked site on the Internet
• gt 170 million users
• 1 photo sharing site
• 4th most trafficked site on the Internet
• 114 user growth in 2008
• gt 800 thousand users
• 1,689 user growth in 2008

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High Level Graph Statistics

• Based on Facebooks 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.
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Social Degree CDF
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Nodes vs. Total Interactions

• Social degree does not accurately predict human
  behavior
• Interactions are highly skewed towards a small
  percent of the Facebook population