Seminar Series: Cosma Shalizi

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Cosma Shalizi
January 21, 2021
3:00PM - 4:00PM
Location
Virtual Event

Date Range
Add to Calendar 2021-01-21 15:00:00 2021-01-21 16:00:00 Seminar Series: Cosma Shalizi Title Nonparametric Estimation and Comparison for Networks Meeting Link 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.) Virtual Event Department of Statistics webmaster@stat.osu.edu America/New_York public
Description

Title

Nonparametric Estimation and Comparison for Networks

Meeting Link

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.)