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B1416
Title: Modelling sparsity, heterogeneity, reciprocity and community structure in temporal interaction data Authors:  Francois Caron - University of Oxford (United Kingdom)
Yee Whye Teh - Oxford University (United Kingdom)
Xenia Miscouridou - Oxford (United Kingdom) [presenting]
Abstract: A novel class of network models for temporal dyadic interaction data is proposed. The goal is to capture a number of important features often observed in social interactions: sparsity, degree heterogeneity, community structure and reciprocity. We propose a family of models based on self-exciting Hawkes point processes in which events depend on the history of the process. The key component is the conditional intensity function of the Hawkes Process, which captures the fact that interactions may arise as a response to past interactions (reciprocity), or due to shared interests between individuals (community structure). In order to capture the sparsity and degree heterogeneity, the base (non time dependent) part of the intensity function builds on compound random measures. We conduct experiments on a variety of real-world temporal interaction data and show that the proposed model outperforms many competing approaches for link prediction, and leads to interpretable parameters.