nips nips2009 nips2009-174 nips2009-174-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Kurt Miller, Michael I. Jordan, Thomas L. Griffiths
Abstract: As the availability and importance of relational data—such as the friendships summarized on a social networking website—increases, it becomes increasingly important to have good models for such data. The kinds of latent structure that have been considered for use in predicting links in such networks have been relatively limited. In particular, the machine learning community has focused on latent class models, adapting Bayesian nonparametric methods to jointly infer how many latent classes there are while learning which entities belong to each class. We pursue a similar approach with a richer kind of latent variable—latent features—using a Bayesian nonparametric approach to simultaneously infer the number of features at the same time we learn which entities have each feature. Our model combines these inferred features with known covariates in order to perform link prediction. We demonstrate that the greater expressiveness of this approach allows us to improve performance on three datasets. 1
[1] Stanley Wasserman and Katherine Faust. Social Network Analysis: Methods and Applications. Cambridge University Press, 1994.
[2] Stanley Wasserman and Philippa Pattison. Logit models and logistic regressions for social networks: I. an introduction to Markov random graphs and p∗ . Psychometrika, 61(3):401–425, 1996.
[3] Garry Robins, Tom Snijders, Peng Wang, Mark Handcock, and Philippa Pattison. Recent developments in exponential random graph (p*) models for social networks. Social Networks, 29(2):192–215, May 2007.
[4] Yuchung J. Wang and George Y. Wong. Stochastic blockmodels for directed graphs. Journal of the American Statistical Association, 82(397):8–19, 1987.
[5] Krzysztof Nowicki and Tom A. B. Snijders. Estimation and prediction for stochastic blockstructures. Journal of the American Statistical Association, 96(455):1077–1087, 2001.
[6] Charles Kemp, Joshua B. Tenenbaum, Thomas L. Griffiths, Takeshi Yamada, and Naonori Ueda. Learning systems of concepts with an infinite relational model. In Proceedings of the American Association for Artificial Intelligence (AAAI), 2006.
[7] Zhao Xu, Volker Tresp, Kai Yu, and Hans-Peter Kriegel. Infinite hidden relational models. In Proceedings of the 22nd Conference on Uncertainty in Artificial Intelligence (UAI), 2006.
[8] Edoardo M. Airoldi, David M. Blei, Eric P. Xing, and Stephen E. Fienberg. Mixed membership stochastic block models. In D. Koller, Y. Bengio, D. Schuurmans, and L. Bottou, editors, Advances in Neural Information Processing Systems (NIPS) 21. Red Hook, NY: Curran Associates, 2009.
[9] Peter D. 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.
[10] Peter D. Hoff. Bilinear mixed-effects models for dyadic data. Journal of the American Statistical Association, 100(469):286–295, 2005.
[11] Peter D. Hoff. Multiplicative latent factor models for description and prediction of social networks. Computational and Mathematical Organization Theory, 2008.
[12] Thomas L. Griffiths and Zoubin Ghahramani. Infinite latent feature models and the Indian Buffet Process. In Y. Weiss, B. Sch¨ lkopf, and J. Platt, editors, Advances in Neural Information Processing Systems o (NIPS) 18. Cambridge, MA: MIT Press, 2006.
[13] Edward Meeds, Zoubin Ghahramani, Radford Neal, and Sam Roweis. Modeling dyadic data with binary latent factors. In B. Sch¨ lkopf, J. Platt, and T. Hofmann, editors, Advances in Neural Information o Processing Systems (NIPS) 19. Cambridge, MA: MIT Press, 2007.
[14] Daniel L. Navarro and Thomas L. Griffiths. Latent features in similarity judgment: A nonparametric Bayesian approach. Neural Computation, 20(11):2597–2628, 2008.
[15] Christian P. Robert and George Casella. Monte Carlo Statistical Methods. Springer, 2004.
[16] Radford M. Neal. Markov chain sampling methods for Dirichlet process mixture models. Journal of Computational and Graphical Statistics, 9(2):249–265, 2000.
[17] James H. Albert and Siddhartha Chib. Bayesian analysis of binary and polychotomous response data. Journal of the American Statistical Association, 88(422):669–679, 1993.
[18] Dilan G¨ r¨ r, Frank J¨ kel, and Carl Edward Rasmussen. A choice model with infinitely many latent ou a features. In Proceedings of the 23rd International Conference on Machine learning (ICML), 2006.
[19] Rudolph J. Rummel. Dimensionality of nations project: Attributes of nations and behavior of nation dyads, 1950–1965. ICPSR data file, 1999.
[20] Woodrow W. Denham. The Detection of Patterns in Alyawarra Nonverbal Behavior. PhD thesis, University of Washington, 1973.
[21] Jin Huang and Charles X. Ling. Using AUC and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering, 17(3):299–310, 2005.
[22] Amir Globerson, Gal Chechik, Fernando Pereira, and Naftali Tishby. Euclidean embedding of cooccurrence data. The Journal of Machine Learning Research, 8:2265–2295, 2007. 9