Date: 2017-01-13

Time: 15:30-16:30

Location: BURN 1205

Abstract:

Many popular statistical models for network valued datasets fall under the remit of the graphon framework, which (implicitly) assumes the networks are densely connected. However, this assumption rarely holds for the real-world networks of practical interest. We introduce a new class of models for random graphs that generalises the dense graphon models to the sparse graph regime, and we argue that this meets many of the desiderata one would demand of a model to serve as the foundation for a statistical analysis of real-world networks. The key insight is to define the models by way of a novel notion of exchangeability; this is analogous to the specification of conditionally i.i.d. models by way of de Finetti’s representation theorem. We further develop this model class by explaining the foundations of sampling and estimation of network models in this setting. The later result can be can be understood as the (sparse) graph analogue of estimation via the empirical distribution in the i.i.d. sequence setting.

Speaker

Victor Veitch is a PhD candidate in the Department of Statistical Sciences at the University of Toronto working in the group of Daniel Roy. He is interested in the theory and application of machine learning and statistical inference, with a particular focus on Bayesian non-parametrics and random networks.