|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 listsSwitch Off Week Faraday Institute CSC Lectures on Human Development
Other talksProf. Jules Hoffman - Title to be confirmed Energy transfer and quantum effects on rate processes Resource Allocation for Statistical Estimation Modelling Mechanical Properties of Carbon Nanotube Fibres What Does China Want? Spectral Imaging Methods To Improve Cancer Diagnosis