Supervised link prediction using multiple sources

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2010, Pages 923-928

  Lu, Zhengdong ^a   Savas, Berkant ^b   Tang, Wei ^c   Dhillon, Inderjit S ^b,c  

^a MICROSOFT RESEARCH ASIA   (China)

^b UNIVERSITY OF TEXAS AT AUSTIN   (United States)

^c University of Texas at Austin   (United States)

Author keywords

Link prediction; Multiple sources; Social network; Supervised learning

Indexed keywords

APPLICATION DOMAINS; AUXILIARY NETWORK; COLLABORATION NETWORK; COMMERCIAL APPLICATIONS; DESIGN SCHEME; FACEBOOK; FEATURE SELECTION; FUNDAMENTAL PROBLEM; LINK PREDICTION; MULTIPLE SOURCE; PATH-BASED; PREDICTION ACCURACY; PROXIMITY NETWORKS; REAL-WORLD; RESEARCH APPROACH; SOCIAL NETWORK; SOCIAL NETWORK ANALYSIS; SOCIAL NETWORKS; TOPOLOGICAL STRUCTURE;

DATA MINING; ELECTRIC NETWORK ANALYSIS; FEATURE EXTRACTION; SOCIAL NETWORKING (ONLINE); SUPERVISED LEARNING;

FORECASTING;

EID: 79951753036     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2010.112     Document Type: Conference Paper

References (21)

1. L. Adamic and E. Adar. Friends and neighbors on the web. Social networks, 25:211-230, 2003. (Pubitemid 36831776)
2. F. Bach. Exploring large feature spaces with hierarchical multiple kernel learning. In NIPS, 2008.
3. A. Barabasi, H. Jeong, Z. Neda, E. Ravasz, A. Schubert, and T. Vicsek. Evolution of the social network of scientific collaborations. Physica A: Statistical Mechanics and its Applications, 311:590-614, 2002. (Pubitemid 34782622)
4. J. Guiver and E. Snelson. Bayesian inference for Plackett-Luce ranking models. In ICML, 2009.
5. S. Hanneke, W. Fu, and E. Xing. Discrete temporal models of social networks. Electronic Journal of Statistics, 4(2010) 585-605, 2010.
6. F. Harary. Graph Theory. Westview Press, 1994.
7. M. Hasan, V. Chaoji, S. Salem, and M. Zaki. Link prediction using supervised learning. In Workshop on Link Analysis, Counterterrorism and Security (SDM), 2006.
8. T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning. Springer-Verlag, New York, 2001.
9. Z. Huang. Link prediction based on graph topology: The predictive value of the generalized clustering coefficient. In Workshop on Link Analysis (KDD), 2006.
10. H. Kashima, T. Kato, Y. Yamanishi, M. Sugiyama, and K. Tsuda. Link propagation: A fast semi-supervised learning algorithm for link prediction. In SDM, 2009.
11. L. Katz. A new status index derived from sociometric analysis. Psychometrika, 18:39-43, 1953.
12. J. Kunegis and A. Lommatzsch. Learning spectral graph transformations for link prediction. In ICML, 2009.
13. J. Leskovec, L. Backstrom, R. Kumar, and A. Tomkins. Microscopic evolution of social networks. In KDD, 2008.
14. D. Liben-Nowell and J. Kleinberg. The link prediction problem for social networks. In CIKM, 2003.
15. Z. Lu, B. Savas, W. Tang, and I. S. Dhillon. Supervised link prediction using multiple sources. Technical Report TR-10-35, Department of Computer Science, UT Austin, 2010.
16. M. Mitzenmacher. A brief history of generative models for power law and lognormal distributions. Internet Mathematics, 1(2):226-251, 2004.
17. M. Newman. Clustering and preferential attachment in growing networks. Physical Review E, 64(025102), 2001.
18. G. Robins and P. Pattison. Random graph models for temporal processes in social networks. Journal of Mathematical Sociology, 25:5-41, 2001. (Pubitemid 33397469)
19. H. H. Song, T. W. Cho, V. Dave, Y. Zhang, and L. Qiu. Scalable proximity estimation and link prediction in online social networks. In IMC, 2009.
20. B. Taskar, M.-F. Wong, P. Abbeel, and D. Koller. Link prediction in relational data. In NIPS, 2003.
21. P. Zhao, G. Rocha, and B. Yu. Grouped and hierarchical model selection through composite absolute penalties. Annals of Statistics, 37:3468-3497, 2009.