|COOKIES: By using this website you agree that we can place Google Analytics Cookies on your device for performance monitoring.|
Modelling Reciprocating Relationships with Hawkes Processes
If you have a question about this talk, please contact Zoubin Ghahramani.
We present a Bayesian nonparametric model that discovers implicit social structure from interaction time-series data. Social groups are often formed implicitly, through actions among members of groups. Yet many models of social networks use explicitly declared relationships to infer social structure. We consider a particular class of Hawkes processes, a doubly stochastic point process, that is able to model reciprocity between groups of individuals. We then extend the Inﬁnite Relational Model by using these reciprocating Hawkes processes to parameterise its edges, making events associated with edges co-dependent through time. Our model outperforms general, unstructured Hawkes processes as well as structured Poisson process-based models at predicting verbal and email turn-taking, and military conﬂicts among nations.
This talk is part of the Machine Learning @ CUED series.
This talk is included in these lists:
Note that ex-directory lists are not shown.
Other listsCambridge University Anthropological Society CUQM Lectures Creative Research at Museum of Archaeology & Anthropology
Other talksProfessor Doug Winton - Title tbc Nanoparticle based analysis of biomolecules, cells and tissue A Sri Lankan evening in conjunction with the University Language Centre Whipple Museum of the History of Science - Private tour Common Law and the origins of shareholder protection Disease dynamics in animal hosts: How natural selection affects disease transmission in insects; and how animal density and climatic factors can influence the prevalence of zoonotic diseases