Title: Online Estimation and Network Point Processes

Abstract: A common goal in network modeling is to uncover the latent structure present among nodes. For many real-world networks the true connections consist of events arriving as streams, which are then aggregated to form edges, ignoring the dynamic temporal component. A natural way to take account of the temporal dynamics of interactions is to use point processes as the foundation of network models. Computational complexity can hamper the scalability of such methods to large sparse networks which occur in modern settings. We consider the use of online variational inference as a way of scaling such methods when the goal is community detection.