A dynamic relational infinite feature model for longitudinal social networks

Journal of Machine Learning Research

Volumn 15, Issue , 2011, Pages 287-295

  Foulds, James ^a   DuBois, Christopher ^b   Asuncion, Arthur U ^a   Butts, Carter T ^c   Smyth, Padhraic ^a  

^a Irvine   (United States)

^b 5226 Donald Bren Hall   (United States)

^c UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

FEATURE MODELS; NON-PARAMETRIC; PREDICTION TASKS; REAL WORLD DATA; RELATIONAL DATA; SIMILAR MODELS; SOCIAL NETWORKS; STATIC NETWORKS;

ARTIFICIAL INTELLIGENCE; BAYESIAN NETWORKS; MARKOV PROCESSES;

SOCIAL NETWORKING (ONLINE);

EID: 84862284477     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Conference Paper

References (21)

1. E.M. Airoldi, D.M. Blei, S.E. Feinberg, and E.P. Xing. Mixed membership stochastic blockmodels. Journal of Machine Learning Research, 9:1981-2014, 2008.
2. C.T. Butts. A relational event framework for social action. Sociological Methodology, 38(1):155-200, 2008.
3. J. Chang and D.M. Blei. Relational topic models for document networks. Proceedings of the International Conference on Artificial Intelligence and Statistics, 2009.
4. Steven E. Fienberg and Stanley Wasserman. Categorical data analysis of single sociometric relations. Sociological Methodology, 12:156-192, 1981.
5. Wenjie Fu, Le Song, and Eric P. Xing. Dynamic mixed membership blockmodel for evolving networks. Proceedings of the 26th Annual International Conference on Machine Learning - ICML '09, pages 1-8, 2009.
6. T. Griffiths and Z Ghahramani. Infinite latent feature models and the Indian buffet process. Advances in Neural Information Processing Systems, 18:475-482, 2006.
7. Mark Handcock, Garry Robins, Tom Snijders, and Julian Besag. Assessing degeneracy in statistical models of social networks. Journal of the American Statistical Association, 76:33-50, 2003.
8. Peter Hoff. Modeling homophily and stochastic equivalence in symmetric relational data. In Advances in Neural Information Processing Systems 20, 2007.
9. Peter Hoff. Multiplicative latent factor models for description andprediction of social networks. Computational and Mathematical Organization Theory, 15(4):261-272, October 2008.
10. Peter Hoff, Adrian E Raftery, and Mark S Handcock. Latent space approaches to social network analysis. Journal of the American Statistical Association, 97(460):1090-1098, 2002.
11. C. Kemp, J.B. Tenenbaum, T.L. Griffiths, T. Yamada, and N. Ueda. Learning systems of concepts with an infinite relational model. In Proceedings of the Twenty-First National Conference on Artificial Intelligence, 2006.
12. B. Klimt and Y. Yang. Introducing the Enron corpus. In First Conference on Email and Anti-Spam (CEAS), 2004.
13. Edward Meeds, Zoubin Ghahramani, Radford Neal, and Sam Roweis. Modeling dyadic data with binary latent factors. In Advances in neural information processing systems, 2007.
14. K.T. Miller, T.L. Griffiths, and M.I. Jordan. Nonparametric latent feature models for link prediction. In Advances in Neural Information Processing Systems (NIPS), 2009.
15. Krzysztof Nowicki and Tom A B Snijders. Estimation and prediction of stochastic blockstructures. Journal of the American Statistical Association, 96(455):1077-1087, 2001.
16. P. Sarkar and A.W. Moore. Dynamic social network analysis using latent space models. SIGKDD Explorations: Special Edition on Link Mining, 7(2):31-40, 2005.
17. P. Sarkar, S.M. Siddiqi, and G.J. Gordon. A latent space approach to dynamic embedding of co-occurrence data. In Proceedings of the International Conference on Artificial Intelligence and Statistics, 2007.
18. S.L. Scott. Bayesian hidden Markov models : Recursive computing in the 21st century. Journal of the American Statistical Association, 97(457):337-351, 2002.
19. T.A.B. Snijders. Statistical methods for network dynamics. In Proceedings of the XLIII Scientific Meeting, Italian Statistical Society, pages 281-296, 2006.
20. Y.W. Teh, D. Görür, and Z. Ghahramani. Stick-breaking construction for the Indian buffet process. In Proceedings of the International Conference on Artificial Intelligence and Statistics, 2007.
21. J. Van Gael, Y.W. Teh, and Z. Ghahramani. The infinite factorial hidden Markov model. In Advances in Neural Information Processing Systems, volume 21, pages 1697-1704, 2009.