Bayesian Poisson Tucker Decomposition for Learning the Structure of International Relations
This work addresses the challenge of analyzing complex international relations data for researchers in political science and machine learning, though it appears incremental as it builds on existing decomposition methods.
The authors tackled the problem of modeling country-country interaction event data by introducing Bayesian Poisson Tucker decomposition (BPTD), which discovers overlapping country-community memberships and directed community-community networks specific to topics and temporal regimes, achieving better predictive performance than related models.
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.