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Bayesian Poisson Tensor Decomposition for International Relations

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SNAW04 - Dynamic Networks

Like their inhabitants, countries interact with one another: theyconsult, negotiate, trade, threaten, and fight. These interactions areseldom uncoordinated. Rather, they are connected by a fabric ofoverlapping communities, such as security coalitions, treaties, tradecartels, and military alliances. A single country can belong tomultiple communities, reflecting its many overlapping identities, andcan engage in both within- and between-community interactions,depending on the capacity in which it is acting. In this talk, I willintroduce two tensor decomposition models for modeling interactionevents of the form “country i took action a toward country j at timet.” The first model (Bayesian Poisson CP decomposition) discoverscoherent threads of events, characterized by sender countries,receiver countries, action types, and time steps; the second model(Bayesian Poisson Tucker decomposition) discovers latentcountry—community memberships, including the number of latentcommunities, as well as directed community—community interactionnetworks that are specific to “topics” of similar action types. I willdemonstrate that these models infer interpretable latent structuresthat conform to and inform our knowledge of international relations.Many existing models for discrete data (such as networks and text) arespecial cases of these models, including infinite relational models,stochastic block models, and latent Dirichlet allocation. As a result,Bayesian Poisson tensor decomposition is a general framework foranalyzing and understanding discrete data sets in the social sciences.

This talk is part of the Isaac Newton Institute Seminar Series series.

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