Title
Nonparametric Estimation and Comparison for Networks
Speaker
Cosma Shalizi - Carnegie Mellon University, Department of Statistics
Abstract
Scientific questions about networks are often comparative: we want to know whether the difference between two networks is just noise, and, if not, how their structures differ. I'll describe a general framework for network comparison, based on testing whether the distance between models estimated from separate networks exceeds what we'd expect based on a pooled estimate. This framework is especially powerful with nonparametric network models, such as densities of latent node locations, or continuous generalizations of block models ("graphons"); the estimation methods for those models also let us generate surrogate data, predict links, and summarize structure. (Joint work with Dena Asta, Brian Karrer, and Lawrence Wang.)
