Mixed Membership Stochastic Blockmodels


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 latent variable model of such data called the mixed membership stochastic blockmodel. 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 and protein interaction networks.

DOI: 10.1145/1390681.1442798

Extracted Key Phrases

4 Figures and Tables

Showing 1-10 of 11 references

A brief review of some recent research In: Statistical Network Analysis: Models, Issues and New Directions

  • S Wasserman, G Robins, D Steinley
  • 2007
1 Excerpt

Discussion of " Model-based clustering for social networks

  • P Doreian, V Batagelj, A Ferligoj
  • 2007
Showing 1-10 of 602 extracted citations
Citations per Year

1,087 Citations

Semantic Scholar estimates that this publication has received between 941 and 1,256 citations based on the available data.

See our FAQ for additional information.