"Sparse Graph Limits with Applications to Machine Learning" (Week after Next at the Statistics Seminar)

Attention conservation notice: Notice of an upcoming academic talk at Carnegie Mellon. Only of interest if you (1) care about how the mathematics of graph limits intersects with non-parametric network modeling, and (2) will be in Pittsburgh week after next.

Jennifer Chayes, "Sparse Graph Limits with Applications to Machine Learning"

Abstract: We introduce and develop a theory of limits for sequences of sparse graphs based on $L^p$ graphons, which generalizes existing theories of graph limits, and in particular includes graphs with power-law degree distributions. We then apply these results to nonparametric stochastic block models, which are used by statisticians to analyze large networks. We use our sparse graph limit results to derive strong results on estimation of functions characterizing these nonparametric stochastic block models. This talk assumes no prior knowledge of graphons or stochastic block models. The talk represents joint work with Christian Borgs, Henry Cohn, Shirshendu Ganguly, and Yufei Zhao.

Time and place: 4 pm on Monday, 20 April 2015, in Scaife Hall 125

As always, the talk is free and open to the public.

(I'd write something long here about why graph limits are so interesting, but why repeat myself?)

Networks; Enigmas of Chance