- Bayesian Anal.
- Advance publication (2018), 26 pages.
Mixed Membership Stochastic Blockmodels for Heterogeneous Networks
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.
Bayesian Anal., Advance publication (2018), 26 pages.
First available in Project Euclid: 19 June 2019
Permanent link to this document
Digital Object Identifier
Huang, Weihong; Liu, Yan; Chen, Yuguo. Mixed Membership Stochastic Blockmodels for Heterogeneous Networks. Bayesian Anal., advance publication, 19 June 2019. doi:10.1214/19-BA1163. https://projecteuclid.org/euclid.ba/1560909813
- Supplementary Material for “Mixed Membership Stochastic Blockmodels for Heterogeneous Networks”. The supplementary material contains the details of the variational posterior inference, variational EM algorithm, and the proofs of theoretical results in Section 4.