Part of Advances in Neural Information Processing Systems 21 (NIPS 2008)
Edo M. Airoldi, David Blei, Stephen Fienberg, Eric Xing
Observations consisting of measurements on relationships for pairs of objects arise in many settings, such as protein interaction and gene regulatory networks, collections of author-recipient email, and social networks. Analyzing such data with probabilisic models can be delicate because the simple exchangeability assumptions underlying many boilerplate models no longer hold. In this paper, we describe a class of latent variable models of such data called Mixed Membership Stochastic Blockmodels. This model extends blockmodels for relational data to ones which capture mixed membership latent relational structure, thus providing an object-specific low-dimensional representation. We develop a general variational inference algorithm for fast approximate posterior inference. We explore applications to social networks and protein interaction networks.