Using Transactional Information to Predict Link Strength in Online Social Networks

Proceedings of the 3rd International AAAI Conference on Weblogs and Social Media, ICWSM 2009

Volumn , Issue , 2009, Pages 74-81

  Kahanda, Indika ^a   Neville, Jennifer ^b,c  

^a Purdue University   (United States)

^b Purdue University   (United States)

^c PURDUE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

FORECASTING; ONLINE SYSTEMS;

ATTRIBUTE-BASED; FACEBOOK; FILE TRANSFERS; LINK PREDICTION; NETWORK FEATURES; PREDICTION TASKS; PUBLIC DATA; SCIENTIFIC FIELDS; SUPERVISED LEARNING APPROACHES;

SOCIAL NETWORKING (ONLINE);

EID: 84909989330     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1609/icwsm.v3i1.13957     Document Type: Conference Paper

References (24)

1. Adamic, A., and Adar, E. 2003. Friends and neighbors on the web. social networks. Social Networks 25(2):211230.
2. Brandes, U. 2001. A faster algorithm for betweenness centrality. Journal of Mathematical Sociology 25:163-177. Breiman, L. 1996. Bagging predictors. Machine Learning 24(2):123-140.
3. Chakrabarti, S.; Dom, B.; and Indyk, P. 1998. Enhanced hypertext categorization using hyperlinks. In Proceedings of the ACM SIGMOD International Conference on Management of Data, 307-318.
4. Domingos, P., and Richardson, M. 2001. Mining the network value of customers. In Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 57-66.
5. Girvan, M., and Newman, M. E. J. 2002. Community structure in social and biological networks. Proceedings of the National Academy of Sciences 99(12):7821-7826.
6. Granovetter, M. 1983. The strength of weak ties: A network theory revisited. Sociological Theory 1:201-233.
7. Harris, K. 2008. The national longitudinal study of adolescent health (add health), waves i & ii, 1994 1996; wave iii, 20012002 [machine-readable data file and documentation]. Chapel Hill, NC: Carolina Population Center, University of North Carolina at Chapel Hill.
8. Hasan, M.; Chaoji, V.; Salem, S.; and Zaki, M. 2005. Link prediction using supervised learning. In In Proceedings of the Workshop on Link Discovery: Issues, Approaches and Applications.
9. Jensen, D.; Neville, J.; and Gallagher, B. 2004. Why collective inference improves relational classification. In Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 593-598.
10. Kashima, H., and Abe, N. 2006. A parameterized probabilistic model of network evolution for supervised link prediction. In In Proceedings of the Sixth IEEE International Conference on Data Mining.
11. Kumar, R.; Novak, J.; and Tomkins, A. 2006. Structure and evolution of online social networks. In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 611-617. New York, NY, USA: ACM.
12. Lanckriet, G.; Bie, T. D.; Cristianini, N.; Jordan, M.; and Noble, W. 2004. A statistical framework for genomic data fusion. Bioinformatics 20(16):2626-2635.
13. Liben-Nowell, D., and Kleinberg, J. 2004. The link prediction problem for social networks. In In Proceedings of the Twelfth International Conference on Information and Knowledge Management (CIKM).
14. McGovern, A.; Friedland, L.; Hay, M.; Gallagher, B.; Fast, A.; Neville, J.; and Jensen, D. 2003. Exploiting relational structure to understand publication patterns in high-energy physics. SIGKDD Explorations 5(2):165-172.
15. McPherson, M.; Smith-Lovin, L.; and Cook, J. 2001. Birds of a feather: Homophily in social networks. Annual Review of Sociology 27:415-445.
16. Neville, J.; Simsek, O.; Jensen, D.; Komoroske, J.; Palmer, K.; and Goldberg, H. 2005. Using relational knowledge discovery to prevent securities fraud. In Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 449-458.
17. O'Madadhain, J.; Hutchins, J.; and Smyth, P. 2005. Prediction and ranking algorithms for event-based network data. SIGKDD Explorations 7(2):2330.
18. Rattigan, M., and Jensen, D. 2005. The case for anomalous link discovery. SIGKDD Explorations 7(2):41-47.
19. Robins, G.; Snijders, T.; Wang, P.; Handcock, M.; and Pattison, P. 2007. Recent developments in exponential random graph (p*) models for social networks. Social Networks 29:192-215.
20. Sharan, U., and Neville, J. 2008. Temporal-relational classifiers for prediction in evolving domains. In Proceedings of the 8th IEEE International Conference on Data Mining.
21. Taskar, B.; Wong, M. F.; Abbeel, P.; and Koller, D. 2003. Link prediction in relational data. In In Proceedings of the Neural Information Processing Systems Conference (NIPS03).
22. Wasserman, S., and Faust, K. 1994. Social Network Analysis: Methods and Applications. Cambridge, UK: Cambridge University Press.
23. Witten, I., and Frank, E. 2005. Data Mining: Practical Machine Learning Tools and Techniques. San Francisco: Morgan Kaufmann.
24. Xu, Z.; King, I.; and Lyu, M. 2007. Web page classification with heterogeneous data fusion. In Proceedings of the World Wide Web Conference.