- Bayesian Anal.
- Advance publication (2018), 25 pages.
Detecting Structural Changes in Longitudinal Network Data
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.
Bayesian Anal., Advance publication (2018), 25 pages.
First available in Project Euclid: 22 February 2019
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Park, Jong Hee; Sohn, Yunkyu. Detecting Structural Changes in Longitudinal Network Data. Bayesian Anal., advance publication, 22 February 2019. doi:10.1214/19-BA1147. https://projecteuclid.org/euclid.ba/1550826221