文章基本信息

标题：Detecting Structural Changes in Longitudinal Network Data
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作者：Jong Hee Park ; Yunkyu Sohn
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：1
页码：133-157
DOI：10.1214/19-BA1147
语种：English
出版社：International Society for Bayesian Analysis
摘要：Dynamic modeling of longitudinal networks has been an increasingly important topic in applied research. While longitudinal network data commonly exhibit dramatic changes in its structures, existing methods have largely focused on modeling smooth topological changes over time. In this paper, we develop a hidden Markov network change-point model (HNC) that combines the multilinear tensor regression model (Hoff, 2011) with a hidden Markov model using Bayesian inference. We model changes in network structure as shifts in discrete states yielding particular sets of network generating parameters. Our simulation results demonstrate that the proposed method correctly detects the number, locations, and types of changes in latent node characteristics. We apply the proposed method to international military alliance networks to find structural changes in the coalition structure among nations.
关键词：network latent space; hidden Markov model; WAIC; military alliance