Mixed membership stochastic blockmodels for heterogeneous networks

Weihong Huang, Yan Liu, Yuguo Chen

Research output: Contribution to journalArticlepeer-review


Heterogeneous networks are useful for modeling complex systems that consist of different types of objects. However, there are limited statistical models to deal with heterogeneous networks. In this paper, we propose a statistical model for community detection in heterogeneous networks. We formulate a heterogeneous version of the mixed membership stochastic blockmodel to accommodate heterogeneity in the data and the content dependent property of the pairwise relationship. We also apply a variational algorithm for posterior inference. The proposed procedure is shown to be consistent for community detection under mixed membership stochastic blockmodels for heterogeneous networks. We demonstrate the advantage of the proposed method in modeling overlapping communities and multiple memberships through simulation studies and applications to a real data set.

Original languageEnglish (US)
Pages (from-to)711-736
Number of pages26
JournalBayesian Analysis
Issue number3
StatePublished - 2020


  • Clustering
  • Community detection
  • Heterogeneous network
  • Mixed membership model
  • Stochastic blockmodel
  • Variational algorithm

ASJC Scopus subject areas

  • Statistics and Probability
  • Applied Mathematics


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