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

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

Social Applications 4/2/2009University 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 )  How popular are social applications?  Facebook Platform – 50,000 applications Popular ones have >10 million users each 3

4/2/2009 Social Graphs and User Interactions  Social applications rely on 1. Social graph topology 2. 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

Goals 4/2/2009University 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 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

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

Crawling Facebook for Data 4/2/2009University 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

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 4/2/2009University of California at Santa Barbara 8

FacebookOrkut 1 Number of Users Crawled10,697,0001,846,000  Percentage of Total Users 15%26.9% Number of Social Links Crawled408,265,00022,613,000 Radius9.86 Diameter13.49 Average Path Length Clustering Coefficient Power-law Coefficient α= 1.5, D = 0.55 α= 1.5, D = 0.6 High Level Graph Statistics 4/2/2009University of California at Santa Barbara 9 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 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

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

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 4/2/2009University of California at Santa Barbara 11

Distribution Among Friends 4/2/2009University of California at Santa Barbara 12 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. Social degree does not accurately predict human behavior Initial Question: Are social links valid indicators of real user interaction?  Answer: NO Social degree does not accurately predict human behavior Initial Question: Are social links valid indicators of real user interaction?  Answer: NO

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

A Better Model of Social Graphs 4/2/2009University 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

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} 4/2/2009University of California at Santa Barbara 15

Social vs. Interaction Degree 4/2/2009University of California at Santa Barbara 16 1:1 Degree RatioDunbar’s Number (150)99% of Facebook Users Interaction graph prunes useless edges Results agree with theoretical limits on human social cognition Interaction graph prunes useless edges Results agree with theoretical limits on human social cognition

Interaction Graph Analysis 4/2/2009University of California at Santa Barbara 17 Do Interaction Graphs maintain expected social network graph properties? Social GraphInteraction Graph Number of Vertices10,697,0008,403,000 Number of Edges408,265,00094,665,000 Radius Diameter Average Path Length Clustering Coefficient Power-law Coefficient α= 1.5, D = 0.55 α= 1.5, D = 0.24 Interaction Graphs still have  Power-law scaling  Scale-free behavior  Small-world clustering … But, exhibit less of these characteristics than the full social network Interaction Graphs still have  Power-law scaling  Scale-free behavior  Small-world clustering … But, exhibit less of these characteristics than the full social network

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

Social Applications, Revisited 4/2/2009University 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 in the paper

Sybilguard 4/2/2009University 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

Sybilguard Algorithm 4/2/2009University 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. 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. A B

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

Sybilguard Caveats 4/2/2009University 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

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

Sybilguard on Interaction Graphs 4/2/2009University of California at Santa Barbara 25 When evaluated under real world conditions, performance of social applications changes dramatically

Conclusion 4/2/2009University 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!

Anonymized Facebook data (social graphs and interaction graphs) will be available for download soon at the Current Lab website! Questions? 4/2/ University of California at Santa Barbara

4/2/2009 Social Networks  Social Networks are popular platforms for interaction, communication and collaboration  > 110 million users 9 th most trafficked site on the Internet  > 170 million users #1 photo sharing site 4 th 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

FacebookOrkut 1 Number of Users Crawled10,697,0001,846,000  Percentage of Total Users 15%26.9% Number of Social Links Crawled408,265,00022,613,000 Radius9.86 Diameter13.49 Average Path Length Clustering Coefficient Power-law Coefficient α= 1.5, D = 0.55 α= 1.5, D = 0.6 High Level Graph Statistics 4/2/2009University of California at Santa Barbara A. Mislove, M. Marcon, K. P. Gummadi, P. Druschel, and B. Bhattacharjee. Measurement and analysis of online social networks. In Proc. of IMC, October 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

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

Nodes vs. Total Interactions 4/2/2009University of California at Santa Barbara 31 Top 10% of most well connected users are responsible for 60% of 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 Social degree does not accurately predict human behavior Interactions are highly skewed towards a small percent of the Facebook population