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Contact Prediction, Routing and Fast Information Spreading in Social Networks

Contact Prediction, Routing and Fast Information Spreading in Social Networks. Kazem Jahanbakhsh Computer Science Department University of Victoria August 2012. Outline. Problem Definition and the Context Routing in Mobile Social Settings Human Mobility and Contact Event

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Contact Prediction, Routing and Fast Information Spreading in Social Networks

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

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

  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: • 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. Social-Greedy Routing Algorithms • Approach: a greedy strategy by computing similarities between people social profiles • Social-Greedy I: Sender forwards the message “m” to nodes socially closer to destination. • 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. SDR & Cost Performance Results for Different Routing Schemes (TTL=9h)

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

  8. Contact Graphs Kyle Eric Katy Butters Jack Kenny Sara

  9. Collecting Data from Different Social Settings

  10. Real Data Descriptions

  11. Contact Prediction: Problem Definition and Assumptions

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

  13. Contact Prediction Results Infocom 2006

  14. Hidden Contact Prediction

  15. Hidden Contact Prediction: Reconstruction Algorithm • Methods: • Time-Spatial Locality: NCN, Jacard & MIN • Contact Rates: Popularity • Social Similarity: Foci & Jacard • 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. Hidden Contact Prediction Results Infocom 2006

  17. Supervised Learning Approach Prediction Results (Logistic Regression/KNN) • Techniques: • Logistic Regression • K-Nearest Neighbor (KNN) • Extracted Features: • Contact Graph-based (Degree, Product of degrees, NCN) • Contact Duration • Social Profiles • Static Sensors 17

  18. Fast Information Spreading in Social Networks • 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 18

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

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

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

  22. Hidden Contacts Prediction Results Performance Evaluation (no of external nodes = 73) 0.5 NCN Jac 0.45 Min s e v 0.4 i Pop t i s Rand o 0.35 P e u r 0.3 T f o 0.25 e g a t 0.2 n e c r e 0.15 P e h 0.1 T 0.05 0 3 4 5 6 7 8 9 10 11 12 log Rank 2 MIT Campus 22

  23. Supervised Learning Results Ranking Features 23

  24. Examples of 1-Whiskers 24

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