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Limit Theorems for Joint Embeddings of Multiple Random Networks

Probability Seminar
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Thursday, April 08, 2021
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3:15 pm - 4:15 pm
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Avanti Athreya
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Probability Seminar

Graph embeddings, in which the vertices of a network are mapped
to vectors in a low-dimensional Euclidean space, have gained traction as a basic tool for statistical network inference. We describe a joint---or "omnibus"---embedding in which multiple graphs on the same vertex set are jointly embedded into a single space, with a distinct representation for each graph. We prove a consistency result and a central limit theorem for this omnibus procedure. Through analysis of connectome data, we show that the omnibus embedding can yield insight into network structure at different scales.

Email Jonathan Mattingly jonathan.mattingly@duke.edu for zoom link