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Random walk models of network formation

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SNAW04 - Dynamic Networks

Like their inhabitants, countries interact with one another: they
consult, negotiate, trade, threaten, and fight. These interactions are
seldom uncoordinated. Rather, they are connected by a fabric of
overlapping communities, such as security coalitions, treaties, trade
cartels, and military alliances. A single country can belong to
multiple communities, reflecting its many overlapping identities, and
can engage in both within- and between-community interactions,
depending on the capacity in which it is acting. In this talk, I will
introduce two tensor decomposition models for modeling interaction
events of the form “country i took action a toward country j at time
t.” The first model (Bayesian Poisson CP decomposition) discovers
coherent threads of events, characterized by sender countries,
receiver countries, action types, and time steps; the second model
(Bayesian Poisson Tucker decomposition) discovers latent
country—community memberships, including the number of latent
communities, as well as directed community—community interaction
networks that are specific to “topics” of similar action types. I will
demonstrate that these models infer interpretable latent structures
that conform to and inform our knowledge of international relations.
Many existing models for discrete data (such as networks and text) are
special 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 for
analyzing 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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