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September 2020 Mixed Membership Stochastic Blockmodels for Heterogeneous Networks
Weihong Huang, Yan Liu, Yuguo Chen
Bayesian Anal. 15(3): 711-736 (September 2020). DOI: 10.1214/19-BA1163

Abstract

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.

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Weihong Huang. Yan Liu. Yuguo Chen. "Mixed Membership Stochastic Blockmodels for Heterogeneous Networks." Bayesian Anal. 15 (3) 711 - 736, September 2020. https://doi.org/10.1214/19-BA1163

Information

Published: September 2020
First available in Project Euclid: 19 June 2019

MathSciNet: MR4132647
Digital Object Identifier: 10.1214/19-BA1163

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Vol.15 • No. 3 • September 2020
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