|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 listsMedsin Cambridge Structural Materials Seminar Series In Situ Graduate Colloquium 2013 - Department of Architecture
Other talksProfessor Patrick Cramer - Title to be confirmed Dr Christian Ottmann, Title to be confirmed TBC (oxygen / iron homeostasis and impacts on tumorigenesis and infection) The 2015 Vaccine Summit Task-based language teaching with technology: the EU-Funded CAMELOT project Beating Malaria 2015