Bayesian Nonparametrics for Sparse Dynamic Networks
Konstantina Palla (Microsoft Research Cambridge)
Friday 23rd February, 2018 15:00-16:00 Seminar room 311B
We propose a Bayesian nonparametric prior for time-varying networks. To each node of the network is associated a positive parameter, modelling the sociability of that node. Sociabilities are assumed to evolve over time and are modelled via a dynamic point process model. The model is able to (a) capture smooth evolution of the interaction between nodes, allowing edges to appear/disappear over time (b) capture long term evolution of the sociabilities (c) and yield 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 and apply it to real world datasets.
This is a joint work with Fancois Caron and Yee Whye Teh from the Oxford University.