The document summarizes research on modeling friendships in location-based mobile social networks. It presents a multi-layered approach that combines metrics like user tags, social graph connections, and shared locations to predict potential new friendships. The researchers collected data from the social networking site Brightkite, analyzing over 1.5 million user updates, and tested whether their model using these metrics could accurately recommend new friend links formed in a later period. They found their combined metrics approach outperformed other models and that location data provided important predictive power for friend recommendations.

1. Multi-Layered Friendship Modeling for Location-based Mobile Social Networks Nan Li and Guanling Chen Department of Computer Science, University of Massachusetts Lowell July 14, 2009 Toronto, Canada MobiQuitous 2009

2. Online Social Network Success Popular (half billion ww users) Sticky (26m per day)

3. OSN Goes Mobile Already top Web destinations on smartphones Unique feature – location GPS-enabled phones Sharing current location Attaching location to user-generated content Outlook LSN >$3.3B revenue by 2013 (ABI) Dodgeball, Loopt, Brightkite, Whrrl Google Latitude, Foursquare

4. Brightkite Startup founded 2005, Denver CO Angel funding $1M, 03/2008 Private beta, 04/2008 Opened to public, 10/2008 User activity Check in, status update, photo upload All attached with current location Updates through SMS, Email, Web, iPhone… Social graph with mutual connection See your friends’ or local activity streams

5. Usage Snapshot

6. Contributions Data collection from Brightkite 19k users; 1.5m updates Quantitative correlation model for friendship User tags, social graph, location/activity Evaluation using 10m training data and 45d test data Outperformed than Naïve Bayes classifier or J48 decision tree algorithms

7. Data Collection Brightkite Web APIs 12/9/08-1/9/09: 18,951 active users Back traced to 3/21/08: 1,505,874 updates Profile: age, gender, tags , friends list Social graph: 41,014 nodes and 46,172 links Testing data: next 45 days had 5,098 new links added

9. Tag Cloud

10. Basic Approach Coming up metrics that Differentiate friends and non-friends Tags, social graph, location, activities Combination of the metrics Training and testing with traces

11. Using Metrics

12. Metric Combination

13. Social Graph

14. Social Graph Metric

15. Tag Graph 1000 most popular tags as the nodes Complete graph Link weight reflects likelihood of two tags shared by friends

16. Tag Graph Metric

17. Location Graph

18. Location Graph Metric

19. Rank Value Result

20. Modeling Accuracy Take another 100,000 non-friend pairs Not in training data Plus the newly added 5,098 friend pairs Sort the prediction values

21. ROC Curve

22. Top Recommendations

23. Information Gain Worldwide buzz: Planetary-scale views on an instant-messaging network. J. Leskovec and E. Horvitz, June 2007.

24. Discussions Model stability as Brightkite grows Does not require frequent re-calculation On-demand recommendation Heuristics to speed up metric calculation Possible improvement Different metrics, or combination methods “ Private” updates Conjectured to be few, but no proof

25. Related Work Industrial solutions: Facebook, Twitter Technical details unknown OSN structural analysis Aggregated behavior not suitable for individual recommendations Collective filtering User-item vs. user-user

26. Conclusion Correlated attribute combination has good friendship recommendation power Interests, social graph, location Location metric is important Gender and age not so much Future work System implementation Real-user action-based evaluation

27. Acknowledgement Anonymous reviewers Shepherd- Sharad Agarwal Best Paper Award committee