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A Bayesian nonparametric model for sparse dynamic networks

Presented by: 
Konstantina Palla University of Oxford
Date: 
Tuesday 26th July 2016 - 12:00 to 12:30
Venue: 
INI Seminar Room 1
Abstract: 
Co-authors: Francois Caron (Univ of OXford), Yee Whye Teh (Univ of Oxford)

We propose a Bayesian nonparametric prior for time-varying networks. To each node of the network is associated a positive parameter, modeling the sociability of that nodes. Sociabilities are assumed to evolve over time, and are modeled via a dynamic point process model. The model is able to (a) capture smooth evolution of the interactions between nodes, allowing edges to appear/disappear over time (b) capture long term evolution of the sociabilities of the nodes (c) and yields sparse graphs, where the number of edges grows subquadratically with the number of nodes. The evolution of the sociabilities is described by a tractable time-varying gamma process. We provide some theoretical insights into the model, describe a Hamiltonian Monte Carlo algorithm for efficieent exploration of the posterior distribution and present results on synthetic and real world dataset.
University of Cambridge Research Councils UK
    Clay Mathematics Institute London Mathematical Society NM Rothschild and Sons