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SUMMARY:Bayesian Poisson Tensor Decomposition for International Relations 
 - Hanna Wallach (Microsoft (UK))
DTSTART:20161215T111500Z
DTEND:20161215T120000Z
UID:TALK69512@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION: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 ofoverla
 pping communities\, such as security coalitions\, treaties\, tradecartels\
 , and military alliances. A single country can belong tomultiple communiti
 es\, reflecting its many overlapping identities\, andcan engage in both wi
 thin- and between-community interactions\,depending on the capacity in whi
 ch it is acting. In this talk\, I willintroduce two tensor decomposition m
 odels for modeling interactionevents of the form "country i took action a 
 toward country j at timet." The first model (Bayesian Poisson CP decomposi
 tion) discoverscoherent threads of events\, characterized by sender countr
 ies\,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 dire
 cted community--community interactionnetworks that are specific to "topics
 " of similar action types. I willdemonstrate that these models infer inter
 pretable latent structuresthat conform to and inform our knowledge of inte
 rnational relations.Many existing models for discrete data (such as networ
 ks and text) arespecial cases of these models\, including infinite relatio
 nal 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.<br>
LOCATION:Seminar Room 1\, Newton Institute
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