1. 1 Contact Prediction, Routing and Fast Information Spreading in Social Networks Kazem Jahanbakhsh Computer Science Department University of Victoria August 2012

2. 2Outline Problem Definition and the Context Routing in Mobile Social Settings Human Mobility and Contact Event Collecting Contact Data Contact Prediction Hidden Contact Prediction Fast Information Spreading Conclusions, Major Contributions and Future Work

3. 3 Problem Definition Message routing, human contact prediction and fast information spreading in the context of human social networks.

4. 4 Routing in Mobile Social Settings Motivation : First empirical evaluation of Milgram's experiment in mobile settings Social Profile : Set of social characteristics for a user: o Affiliation, Hometown, Language, Nationality, Interests and so on Goal : Designing an efficient routing algorithm Efficiency : Minimizing message forwardings & Maximizing the probability of message delivery Assumptions & Constraints : Message delivery in physical proximity Sender knows the destination social profile

5. 5 Social-Greedy Routing Algorithms Approach : a greedy strategy by computing similarities between people social profiles o Social-Greedy I : Sender forwards the message “m” to nodes socially closer to destination. o Social-Greedy II & III : Variations of Social-Greedy I. Our work is different from previous work because we only make use of social profiles of people for routing! Real Data : Infocom 2006 contact trace - 79 people - a brief version of social profiles

6. 6 SDR & Cost Performance Results for Different Routing Schemes (TTL=9h)

7. 7 Human Mobility & Contact Data Kenny Eric Eric Kenny 10:00AM 10:10AM Kenny Eric 10:00AM 10:10AM Contact Event: 10:00-10:10 AM 7

8. 8 Contact Graphs Eric Butters Kenny Sara Katy Jack Kyle

9. 9 Collecting Data from Different Social Settings

10. 10 Real Data Descriptions DatasetInf 05Inf 06MITCambRoller Sensors4179973662 Length3 days4 days246 days11 days3 hours Scanning Time120 sec 300 sec600 sec15 sec Ext. Nodes206432120698113671050 Total Cont.2276572821628551241587132511 Ext. Cont.5705657571831353071472365 Ext. Cont. %25%20%64%74%55% DatasetNo. of NodesNo. of Edges Facebook63731817090

11. 11 Contact Prediction: Problem Definition and Assumptions

12. 12 Social Information & Small- World Network Properties Birds of a Feather (Homophily) Using Social Profiles: o Jacard Social Similarity (Jac) o Social Foci Similarity (Foci) o Max Social Similarity (Max) Using Contact Graphs: o Transitivity: Number of Common Neighbors (NCN) o Low Diameter : Shortest Path (SP) Random Walk (RW) How to reconstruct?

13. 13 Contact Prediction Results Infocom 2006

14. 14 Hidden Contact Prediction

15. 15 Hidden Contact Prediction: Reconstruction Algorithm Methods : o Time-Spatial Locality : NCN, Jacard & MIN o Contact Rates : Popularity o Social Similarity : Foci & Jacard o Social Similarity-NCN : Foci-NCN Algorithm : For each compute and store quadruples in Sort in a descending order using similarity scores Output the first number of quadruples

16. 16 Hidden Contact Prediction Results Infocom 2006

17. 17 Supervised Learning Approach Techniques : o Logistic Regression o K-Nearest Neighbor (KNN) Extracted Features : o Contact Graph-based (Degree, Product of degrees, NCN) o Contact Duration o Social Profiles o Static Sensors Session TypeKeynoteLunch BreakCoffee Break TPR0.18/0.240.37/0.400.41/0.43 FPR0.03/0.080.04/0.070.02/0.02 Accuracy81%/78%84%/81%92%/92% RMSE0.42/0.400.39/0.360.26/0.24 Prediction Results (Logistic Regression/KNN) 17

18. 18 Input : social graph G=(V,E) & a unique message for each node Communication Model : synchronized Constraints : no global information & one contact per round Termination : when every node receives all messages Goal : analyzing running times of three information spreading algorithms Fast Information Spreading in Social Networks 18

19. 19 Information Spreading Algorithms Random push-pull : o In each round, every node randomly chooses one of its neighbors for message exchange Doerr : o In each round, every node randomly chooses one of its neighbors except the one that has been just contacted Censor : Hybrid strategy: o Even rounds: each node runs random push-pull o Odd rounds: each node chooses one of its neighbors in a sequential manner from its Bottleneck List

20. 20 Empirical Results from Facebook Graph Running Times Without 1-whiskers Running Times on Original Facebook Graph 20

21. 21 Conclusions & Future Work Major Contributions: Social-Greedy Algorithm : o Suitable for bootstrapping wireless devices Contact Prediction : o Social Similarity methods, SP and RW outperform random o Foci-NCN provides the best precision results o Supervised learning is an effective technique for contact prediction Information spreading : o Censor performs well for spreading information in social networks Future Work : o Proposing more efficient predictors for large geographical spaces o Final Goal: Predicting where people go next and who they will meet there!

22. 22 Hidden Contacts Prediction Results MIT Campus 22 3456789101112 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 Performance Evaluation (no of external nodes = 73) log 2 Rank T h e P e r c e n t a g e o f T r u e P o s i t i v e s NCN Jac Min Pop Rand

23. 23 Supervised Learning Results Session TypeKeynoteLunch BreakCoffee Break degree455 777 degree prod.336 ncn112 total overlap221 social564 ncsn643 Ranking Features 23

24. 24 Examples of 1-Whiskers 24