{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/bayesian-poisson-tensor-factorization-for","title":"Bayesian Poisson Tensor Factorization for Inferring Multilateral Relations from Sparse Dyadic Event Counts","arxiv_id":"1506.03493","date":"2015-06-10","proceeding":null,"authors":["Aaron Schein","John Paisley","David M. Blei","Hanna Wallach"],"abstract":"We present a Bayesian tensor factorization model for inferring latent group\nstructures from dynamic pairwise interaction patterns. For decades, political\nscientists have collected and analyzed records of the form \"country $i$ took\naction $a$ toward country $j$ at time $t$\"---known as dyadic events---in order\nto form and test theories of international relations. We represent these event\ndata as a tensor of counts and develop Bayesian Poisson tensor factorization to\ninfer a low-dimensional, interpretable representation of their salient\npatterns. We demonstrate that our model's predictive performance is better than\nthat of standard non-negative tensor factorization methods. We also provide a\ncomparison of our variational updates to their maximum likelihood counterparts.\nIn doing so, we identify a better way to form point estimates of the latent\nfactors than that typically used in Bayesian Poisson matrix factorization.\nFinally, we showcase our model as an exploratory analysis tool for political\nscientists. We show that the inferred latent factor matrices capture\ninterpretable multilateral relations that both conform to and inform our\nknowledge of international affairs.","url_abs":"http://arxiv.org/abs/1506.03493v1","url_pdf":"http://arxiv.org/pdf/1506.03493v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"bayesian-poisson-tensor-factorization-for","repo_url":"https://github.com/aschein/bptf","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"form","task_name":"Form"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1506.03493","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1506.03493"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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