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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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slide1

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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