|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 lists80,000 Hours - Cambridge Alcuin Lecture Exoplanet Meetings
Other talksDisparities in the evolution of multicellularity The political in question: abolitionism in India's twentieth century Prof. Jules Hoffman - Title to be confirmed The Tropical Tropopause Layer Ionotropic Glutamate receptors and GABAA receptors; from protein structure to neuronal circuits The first CCfCS student symposium