1. User Interactions in Social Networks and their Implications Christo Wilson, Bryce Boe, Alessandra Sala, Krishna P. N. Puttaswamy, Ben Y. Zhao (UC Santa Barbara) EuroSys 2009 May 4, 2011 Hyewon Lim

2. Outline  Introduction  The Facebook Social Network  Data Set and Collection Methodology  Analysis of Social Graphs  Analysis of Interaction Graphs  Conclusion 2

3. Introduction  Social networks –Popular infrastructures for communication, interaction, and information sharing on the Internet  Recent work –Meaningful, interactive relationships with friends are critical to improving trust and reliability in the system  But real-world interpersonal association is not uniform –Social links often connect acquaintances with no level of mutual trust or shared interest 3

4. Introduction Are social links valid indicators of real user interaction? If not, then what can we use to form a more accurate model for evaluating socially-enhanced applications? 4

5. Introduction  Contribution –The first large scale study of the Facebook social network  Users tend to interact mostly with a small subset of friends –Propose the interaction graph –Examine the impact of using different graph models in evaluating socially-enhanced applications 5

6. The Facebook Social Network –The largest social network in the world –Number one photo sharing site on the Internet –Over 150M active users (Feb 2009) –Designed around the concept of networks that organizes users into membership-based groups  Educational institution, company/organization, geographic location –Bidirectional social links by friending other users –Wall, photo uploading, tag, Mini-Feed 6

7. Data Set and Collection Methodology Data Collection Process  Crawled by regional network –Unauthenticated and open to all users –Users belong to at least one regional network –Most users do not modify their default privacy settings  To crawl –Seed: 50 user IDs –Breadth-first searches of social links on each network  Complete data set –Approximately 500GB –Includes full profiles of more than 10 million Facebook users 7

8. Data Set and Collection Methodology Data Collection Process  Primary data set [March – May of 2008] –Profile, Wall and photo data crawled from the 22 largest regional networks  Also performed daily crawls of the San Francisco regional network in Oct 2008 to gather data specifically on the Mini-Feed 8

9. Data Set and Collection Methodology Completeness of Graph Coverage  The majority of user accounts in the social graph are part of a single large, weakly connected component (WCC)  Social links on Facebook are undirected –Breadth first crawling of social links should be able to generate complete coverage of the WCC, assuming that at least one of the initial seeds is linked to the WCC  Validation –Performed five simultaneous crawls of the San Francisco regional network  Start with 50 seeds and going up to 5000 seeds –Difference in the number of users was only 242 users out of approximately 169,000 total 9

10. Data Set and Collection Methodology Description of Collected Data  Collected the full user profile of each user visited during crawls –Also collected full transcripts of Wall posts and photo comments  “Date Joined” –By examining each user’s earliest Wall post  Performed crawls of Mini-Feed data from the San Francisco regional network –To obtain interaction data on Facebook at a more fine-grained level –Crawl daily to ensure that we build up a complete record of each user’s actions on a day-to-day basis  ~400K users 10

11. Analysis of Social Graphs  Analyze general properties of our Facebook population –Including user connectivity in the social graph and growth characteristics over time  Different types of user interactions on Facebook –Including how interactions vary across time applications, and different segments of the user population  Analyze detailed user activities –Through crawls of users Mini-Feed from the San Francisco network –Paying attention to social network growth and interactions over fine- grained time scales 11

12. Analysis of Social Graphs Social Network Analysis  10M users from the 22 largest regional networks –56% of the total user population of those networks  Complete data set [table 1, slide 8] –Over 940M social links –24M interaction events 12

13. Analysis of Social Graphs Social Network Analysis  Social degree analysis –Social degrees on Facebook scale based on a power-law distribution 13

14. Analysis of Social Graphs Social Network Analysis  Social graph analysis –Construct a social graph for each crawled regional network  Limit social graphs to only include links for which users at both end-points were fully visible during crawls  Avg. path leng. ≤ 6 –Lending credence to the six-degrees of separation hypothesis _Milgram 1967  Radius & diameter is similar to the values presented for other SNs –low when compared to other large network graphs, such as the WWW 14

15. Analysis of Social Graphs Social Network Analysis  Clustering coefficient measurements –A measure to determine whether social graphs conform to the small-world principle _Watts 1998 –Defined on an undirected graph as the ratio of the number of links  Exist between a node’s immediate neighborhood and the maximum # of links that could exist –For a node with N neighbors and E edges between those neighbors, CC = (2E) / (N(N-1)) –High CC  Nodes tend to form tightly connected, localized cliques with their immediate neighbors 15

16. Analysis of Social Graphs Social Network Analysis  Clustering coefficient measurements –Avg. CC of Facebook: 0.133 ~ 0.211 (avg over all: 0.167, Orkut: 0.171)  Higher levels of local clustering than either random graphs or random power- law graph, which indicates a tightly clustered fringe that is characteristic of social networks _Mislove 2007 –User w/ lower social degrees have high CC 16

17. Analysis of Social Graphs Social Network Analysis  Assortativity measurement –Assortativity coefficient  A graph measures the probability for nodes in a graph to link to other nodes of similar degree  Calculated as the Pearson correlation coefficient of the degrees of node pairs for all edges in a graph  Result range: -1 ≤ r ≤ 1 –0 ≤ r : nodes tend to connect with other nodes of similar degree –AC for our Facebook graphs are uniformly positive  Connections between high degree nodes in graphs are numerous –This well-connected core of high degree nodes form the backbone of small-world network  AC values closely resemble the those for other large SNs 17

18. Analysis of Social Graphs Social Network Analysis  Growth of Facebook over time –Historical growth of the user population in our sample set  Exponentially increase from month 24 –> 80% of profiles are “young profiles” 18

19. Analysis of Social Graphs User Interaction Analysis  Distribution of the users’ interaction among their friends –The large majority of interactions occur only across a small subset of their social links  Only a subset of social links actually represent interactive relationships 19

20. Analysis of Social Graphs User Interaction Analysis  Interaction distribution among friends –Analyze the user interaction patterns  Photo tags accurately capture real life social situation –Even highly social ones, users show significant skew towards interacting with, and sharing physical proximity with a small subset of their friends 20

21. Analysis of Social Graphs User Interaction Analysis  Distribution of total interactions The bulk of all Facebook interactive events are generated by a small, highly active subset of users  Not all social links are equally useful when Analyzing SNs  A correlation between social degree and interactivity does exist 21

22. Analysis of Social Graphs User Interaction Analysis  Users’ avg. # of interactions at different point in their lifetime –Two possible interpretations  The oldest users were the original users who participated in Facebook’s growth –Self-selected to users highly interested in SNs  Leave only active Facebook users 22

23. Analysis of Social Graphs Mini-feed Analysis  Two missing perspective from Wall and Photo comment data –Do not tell us about the formation of new friend links –Not describe user interactions in other applications –Social graph is growing at a faster rate than users are able to communicate with one another  Average users do not interact with most of the their “Facebook friends” 23

24. Analysis of Interaction Graphs  Interaction Graph Subset of the social graph where for each link, interactivity between the link’s endpoints is greater than the rate stipulated by n and t –n: a minimum number of interaction events –t: a window of time during which interactions must have occurred  n & t delineate an interaction rate threshold  Interaction Degree The number of friends who interact with the user at a rate greater than the parameterized minimum  Implicit assumption underlying our IG –The majority of user interaction events occur across social links 24

25. Analysis of Interaction Graphs  Interaction Graphs on Facebook –Evaluating each user’s incoming and outgoing interactions is challenging –  Sample interactions that occur over social links that connect two users in our user population –Acceptable to model interaction graphs on FB using undirected edges  since this model suits the interactivity patterns of the majority or users 25

26. Analysis of Interaction Graphs  Comparison of Social and Interaction Graphs –Social vs. interaction degree  Interaction degree does not scale equally with social degree 26

27. Analysis of Interaction Graphs  Comparison of Social and Interaction Graphs 27

28. Conclusion  We show… –Interaction activity on Facebook is significantly skewed towards a small portion of each user’s social links  Interaction graph –A more accurate representation of meaningful peer connectivity on SN  Social-based applications should be designed with interactions graphs in mind –Reflect real user activity rather than social linkage alone 28