Bayesian Poisson Tucker Decomposition for Learning the Structure of 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.

To appear in Proceedings of the 33rd International Conference on Machine Learning (ICML 2016)

References in corpus (8)

• Stochastic blockmodels and community structure in networks
• A Review of Relational Machine Learning for Knowledge Graphs
• Bayesian CP Factorization of Incomplete Tensors with Automatic Rank Determination
• An efficient and principled method for detecting communities in networks
• On Tensors, Sparsity, and Nonnegative Factorizations
• Scalable Recommendation with Poisson Factorization
• Infinite Edge Partition Models for Overlapping Community Detection and Link Prediction
• Non-parametric Bayesian modeling of complex networks

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