文章基本信息

标题：Mixed Membership Stochastic Blockmodels for Heterogeneous Networks
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作者：Weihong Huang ; Yan Liu ; Yuguo Chen 等
期刊名称：Bayesian Analysis
印刷版ISSN：1931-6690
电子版ISSN：1936-0975
出版年度：2020
卷号：15
期号：3
页码：711-736
DOI：10.1214/19-BA1163
语种：English
出版社：International Society for Bayesian Analysis
摘要：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.
关键词：clustering; community detection; heterogeneous network; mixed membership model; stochastic blockmodel; variational algorithm