1 / 23

Exploring Social Influence for Link Prediction in Online Social Networks

This workshop delves into social influence and link prediction in online social networks, examining theories like self-interest, contagion, and homophily to predict future links. Data-driven analysis on Anobii.com offers insights into link creation dynamics and network behaviors. The speaker, Luca Maria Aiello from Università degli Studi di Torino, shares findings on dataset analysis, topical overlap, and homophily and influence dynamics. The research aims to understand profile similarity, geographic overlap, and causality between similarity and link creation. Strategies like triadic closure and supervised learning approaches are explored to predict link formation accurately.

monifa
Télécharger la présentation

Exploring Social Influence for Link Prediction in Online Social Networks

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. A glimpse on social influence and link prediction in OSNs Workshop on Data Driven Dynamical Networks Speaker: • Luca Maria Aiello, PhD student • UniversitàdegliStudidi Torino • Computer Science Department • aiello@di.unito.it Keywords : link creation, link prediction, homophily, social influence, aNobii

  2. Acknowledgments Giancarlo Ruffo RossanoSchifanella UniversitàdegliStudidi Torino ISI Foundation Alain Barrat CiroCattuto People: School of Informatics and Computing, Indiana University FilippoMenczer

  3. Dynamics leading to link creation Food networks Collaboration networks Social media 2nd part: exploit the observations on these phenomena to predict future links • Several theories from sociology • Self-interest • Mutual-interest • Exchange • Contagion (influence) • Balance • Homophily • Proximity Les Houches 2010 - Luca Maria Aiello, UniversitàdegliStudidi Torino

  4. Outline • Dataset • Topical overlap • Homophily and influence • Link prediction • Conclusions Les Houches 2010 - Luca Maria Aiello, UniversitàdegliStudidi Torino

  5. Outline • Dataset • Topical overlap • Homophily and influence • Link prediction • Conclusions Les Houches 2010 - Luca Maria Aiello, UniversitàdegliStudidi Torino

  6. Social network for bookworms • Profile features • Library and wishlist • Groups • Tags • Social network • Directed • Friendship + neighborhood • 6 snapshots, 15 days apart • Full giant connected component Data-driven analysis on anobii.com Les Houches 2010 - Luca Maria Aiello, UniversitàdegliStudidi Torino

  7. Basic statistics ng(kout) nb(kout) 103 nw(kout) 102 101 100 103 100 101 102 kout • Broad distributions • Positive correlations between connectivity and activity • Assortativity Les Houches 2010 - Luca Maria Aiello, UniversitàdegliStudidi Torino

  8. Triadic closure • Classification of new links at time t+1 between nodes already present at time t (t ∈ {1,…,5}) Double closure Closure Bidirectional Direct Reciprocated 75% 20% 30% 25% 10% Reciprocation is strong (exchange) Users tend to choose “friends of their friends” as new friends (balance) Les Houches 2010 - Luca Maria Aiello, UniversitàdegliStudidi Torino

  9. Outline • Dataset • Topical overlap • Homophily and influence • Link prediction • Conclusions Les Houches 2010 - Luca Maria Aiello, UniversitàdegliStudidi Torino

  10. Profile similarity vs. social distance Does similarity between user profiles depend on the social distance? • Topical overlap • Statistical correlation because of assortative biases? • Null model to discern real overlap from purely statistical effects • No topical overlap other than that caused by statistical mixing patters Les Houches 2010 - Luca Maria Aiello, UniversitàdegliStudidi Torino

  11. Geographical overlap Null model test with random link rewire Country-level overlap due to language barriers City level overlap SocialCom 2010 - Luca Maria Aiello, Università degli Studi di Torino

  12. Outline • Dataset • Topical overlap • Homophily and influence • Link prediction • Conclusions Les Houches 2010 - Luca Maria Aiello, UniversitàdegliStudidi Torino

  13. Causality between similarity and link creation • What is the cause of topical overlap? • Topical overlap is observed for all profile features • Three possible explanations: • Homophily (people connect with similar people) • Social influence(social connection conveys similarity) • Mixture of the two • Explore the causality relationship between profile similarity and social linking Les Houches 2010 - Luca Maria Aiello, UniversitàdegliStudidi Torino

  14. Similarity  link creation (homophily) Average similarity of pairs forming new links between t and t+1 (t=4), compared with average similarity of all the pairs at distance 2 at time t Pairs that are going to get connected show a substantially higher similarity Les Houches 2010 - Luca Maria Aiello, UniversitàdegliStudidi Torino

  15. Link creation similarity (influence) Groups Books Evolution of the similarity between pairs linking together at different times Les Houches 2010 - Luca Maria Aiello, UniversitàdegliStudidi Torino

  16. Summary Can we exploit the observations on these phenomena to predict future links? • Theories to explain link creation • Self-interest • Mutual-interest • Exchange Reciprocity in linking • Contagion Social influence • Balance Triangle closure • Homophily For all profile features • Proximity Geographical and on social graph Les Houches 2010 - Luca Maria Aiello, UniversitàdegliStudidi Torino

  17. Outline • Dataset • Topical overlap • Homophily and influence • Link prediction • Conclusions Les Houches 2010 - Luca Maria Aiello, UniversitàdegliStudidi Torino

  18. Link prediction Learning set example Snapshots at time t and t+1 Predict links created between t and t+1 given the whole information at time t Supervised learning approach to combine profile and structural features Les Houches 2010 - Luca Maria Aiello, UniversitàdegliStudidi Torino

  19. Features • Structural • Common neighbors • Distance on graph • Preferential attachment • Resource allocation • Local path • Profile • Library (cosine) • Groups (cosine) • Groups (size) • Gender {0,1} • Town {0,1} • Age (|age1 – age2|) • Country {0,1} • Vocabulary (cosine) • Wishlists (cosine) • Tagging behavior Les Houches 2010 - Luca Maria Aiello, UniversitàdegliStudidi Torino

  20. Link prediction: preliminary results • Rotation forest, 10-fold cross-validation, balanced sets • Rotation forest, 10-fold cross-validation, unbalanced sets Les Houches 2010 - Luca Maria Aiello, UniversitàdegliStudidi Torino

  21. Outline • Dataset • Topical overlap • Homophily and influence • Link prediction • Conclusions Les Houches 2010 - Luca Maria Aiello, UniversitàdegliStudidi Torino

  22. Conclusions and future work • Theories on social network growth are verified • Causality between similarity and social connection • Effective link detection/prediction • Topical information seems to be predictive as well as structural information • RFC: • Link prediction sampling/evaluation procedure • New challenges in prediction Les Houches 2010 - Luca Maria Aiello, UniversitàdegliStudidi Torino

  23. Workshop on Data Driven Dynamical Networks Thank you for your attention! Speaker: Luca Maria Aiello aiello@di.unito.it www.di.unito.it/~aiello Reference: L. M. Aiello, A. Barrat, C. Cattuto, G. Ruffo, R. Schifanella"Link creation and profilealignment in the aNobii social network"In SocialCom'10: Proceedingsof the 2nd IEEE International Conference on Social Computing, Minneapolis, MN, USA, August 2010

More Related