Open Access
March 2020 Detecting Structural Changes in Longitudinal Network Data
Jong Hee Park, Yunkyu Sohn
Bayesian Anal. 15(1): 133-157 (March 2020). DOI: 10.1214/19-BA1147


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.


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Jong Hee Park. Yunkyu Sohn. "Detecting Structural Changes in Longitudinal Network Data." Bayesian Anal. 15 (1) 133 - 157, March 2020.


Published: March 2020
First available in Project Euclid: 22 February 2019

zbMATH: 1436.62401
MathSciNet: MR4050880
Digital Object Identifier: 10.1214/19-BA1147

Keywords: Hidden Markov model , military alliance , network latent space , WAIC

Vol.15 • No. 1 • March 2020
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