Nonparametric network comparison
Seminar | August 24 | 4-5 p.m. | 332 Evans Hall
Patrick Wolfe, Purdue University
Understanding how two networks differ, or quantifying the degree to which a single network departs from a given model, is a challenging question in modern mathematical statistics. Here we show how subgraph densities, which for large graphs play a role analogous to moments in the context of random variables, enable a natural means of nonparametric network comparison. Coupled with a partial order derived from a notion of subgraph scale, we then show how this leads to an automated, computationally scalable comparison algorithm with provable properties. Joint work with P.-A. Maugis and S. C. Olhede; preprint at https://arxiv.org/abs/1705.05677.