Mixed membership stochastic blockmodels

TL;DR

This paper introduces the mixed membership stochastic blockmodel, a probabilistic model for relational data that captures complex, overlapping community structures in networks, with a scalable inference algorithm and applications to social and biological networks.

Contribution

It extends traditional blockmodels to allow mixed memberships, providing a flexible, low-dimensional representation of relational data with a variational inference method.

Findings

Effective modeling of social and protein interaction networks.

Scalable variational inference algorithm developed.

Demonstrated advantages over simpler models.

Abstract

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.