Bayesian Poisson Tucker Decomposition for Learning the Structure of International Relations

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Aaron Schein, Mingyuan Zhou, David Blei, Hanna Wallach ;
Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2810-2819, 2016.

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.

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