Detecting structural changes in longitudinal network data

Jong Hee Park, Yunkyu Sohn

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish
Pages (from-to)133-157
Number of pages25
JournalBayesian Analysis
Volume15
Issue number1
DOIs
Publication statusPublished - 2020 Mar 1

Keywords

  • Hidden markov model
  • Military alliance
  • Network latent space
  • Waic

ASJC Scopus subject areas

  • Statistics and Probability
  • Applied Mathematics

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