Bayesian Poisson Tucker Decomposition for Learning the Structure of International Relations

TL;DR

This paper presents Bayesian Poisson Tucker decomposition (BPTD), a novel probabilistic model for analyzing international relations data by uncovering latent community structures, interaction networks, and temporal dynamics with improved inference and interpretability.

Contribution

Introduction of BPTD, a new Bayesian model that captures complex international interaction patterns, including overlapping communities and topic-specific networks, with efficient inference and better predictive accuracy.

Findings

BPTD outperforms related models in prediction tasks.

BPTD discovers interpretable latent structures.

BPTD captures temporal and topical dynamics in international relations.

Abstract

We introduce Bayesian Poisson Tucker decomposition (BPTD) for modeling country--country interaction event data. These data consist of interaction events of the form "country $i$ took action $a$ toward country $j$ at time $t$." BPTD discovers overlapping country--community memberships, including the number of latent communities. In addition, it discovers directed community--community interaction networks that are specific to "topics" of action types and temporal "regimes." We show that BPTD yields an efficient MCMC inference algorithm and achieves better predictive performance than related models. We also demonstrate that it discovers interpretable latent structure that agrees with our knowledge of international relations.