nips nips2008 nips2008-134 nips2008-134-reference knowledge-graph by maker-knowledge-mining
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Author: Edoardo M. Airoldi, David M. Blei, Stephen E. Fienberg, Eric P. Xing
Abstract: In many settings, such as protein interactions and gene regulatory networks, collections of author-recipient email, and social networks, the data consist of pairwise measurements, e.g., presence or absence of links between pairs of objects. Analyzing such data with probabilistic models requires non-standard assumptions, since the usual independence or exchangeability assumptions no longer hold. In this paper, we introduce a class of latent variable models for pairwise measurements: mixed membership stochastic blockmodels. Models in this class combine a global model of dense patches of connectivity (blockmodel) with a local model to instantiate node-specific variability in the connections (mixed membership). We develop a general variational inference algorithm for fast approximate posterior inference. We demonstrate the advantages of mixed membership stochastic blockmodel with applications to social networks and protein interaction networks. 1
[1] D. M. Blei, A. Ng, and M. I. Jordan. Latent Dirichlet allocation. Journal of Machine Learning Research, 3:993–1022, 2003.
[2] P. Doreian, V. Batagelj, and A. Ferligoj. Discussion of “Model-based clustering for social networks”. Journal of the Royal Statistical Society, Series A, 170, 2007.
[3] M. S. Handcock, A. E. Raftery, and J. M. Tantrum. Model-based clustering for social networks. Journal of the Royal Statistical Society, Series A, 170:1–22, 2007.
[4] P. D. Hoff, A. E. Raftery, and M. S. Handcock. Latent space approaches to social network analysis. Journal of the American Statistical Association, 97:1090–1098, 2002.
[5] M. Jordan, Z. Ghahramani, T. Jaakkola, and L. Saul. Introduction to variational methods for graphical models. Machine Learning, 37:183–233, 1999.
[6] C. Kemp, J. B. Tenenbaum, T. L. Griffiths, T. Yamada, and N. Ueda. Learning systems of concepts with an infinite relational model. In Proc. of the 21st National Conference on Artificial Intelligence, 2006.
[7] F.-F. Li and P. Perona. A Bayesian hierarchical model for learning natural scene categories. IEEE Computer Vision and Pattern Recognition, 2005.
[8] K. Nowicki and T. A. B. Snijders. Estimation and prediction for stochastic blockstructures. Journal of the American Statistical Association, 96:1077–1087, 2001.
[9] J. K. Pritchard, M. Stephens, N. A. Rosenberg, and P. Donnelly. Association mapping in structured populations. American Journal of Human Genetics, 67:170–181, 2000.
[10] F. S. Sampson. A Novitiate in a period of change: An experimental and case study of social relationships. PhD thesis, Cornell University, 1968.
[11] S. Wasserman, G. Robins, and D. Steinley. A brief review of some recent research. In: Statistical Network Analysis: Models, Issues and New Directions, Lecture Notes in Computer Science. Springer-Verlag, 2007. 8