From Graphon to Graphex: Models and Estimators for Sparse Networks using Exchangeable Random Measures

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• Daniel Roy (University of Toronto)
• Tuesday 26 July 2016, 14:30-15:00
• Seminar Room 1, Newton Institute.

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SNAW05 - Bayesian methods for networks

A major challenge for Bayesian network analysis is that we lack general nonparametric models of sparse graphs. To meet this challenge, we introduce and study the general class of random graphs defined by the exchangeability of their real-valued vertex labels, an idea inspired by a model due to Caron and Fox. A straightforward adaptation of a result by Kallenberg yields a representation theorem: every such random graph is characterized by three (potentially random) components: a nonnegative real I, an integrable function S : R+ to R+, and a symmetric measurable function W: R+^2 to [0,1] that satisfies several weak integrability conditions. We call the triple (I,S,W) a graphex, in analogy to graphons, which characterize the (dense) exchangeable graphs on the  naturals. I will present some results about the structure and consistent estimation of these random graphs. This is joint work with Victor Veitch.

This talk is part of the Isaac Newton Institute Seminar Series series.

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