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Main Authors: Dominguez, Tomas, Mourrat, Jean-Christophe
Format: Preprint
Published: 2024
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Online Access:https://arxiv.org/abs/2406.15233
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author Dominguez, Tomas
Mourrat, Jean-Christophe
author_facet Dominguez, Tomas
Mourrat, Jean-Christophe
contents We consider the problem of recovering the community structure in the stochastic block model. We aim to describe the mutual information between the observed network and the actual community structure as the number of nodes diverges while the average degree of a given node remains bounded. Our main contribution is a representation of the limit of this quantity, assuming it exists, as an explicit functional evaluated at a critical point of that functional. While we mostly focus on the two-community setting for clarity, we expect our method to be robust to model generalizations. We also present an example involving four communities where we show the invalidity of a plausible candidate variational formula for this limit.
format Preprint
id arxiv_https___arxiv_org_abs_2406_15233
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Critical point representation of the mutual information in the sparse stochastic block model
Dominguez, Tomas
Mourrat, Jean-Christophe
Probability
Information Theory
We consider the problem of recovering the community structure in the stochastic block model. We aim to describe the mutual information between the observed network and the actual community structure as the number of nodes diverges while the average degree of a given node remains bounded. Our main contribution is a representation of the limit of this quantity, assuming it exists, as an explicit functional evaluated at a critical point of that functional. While we mostly focus on the two-community setting for clarity, we expect our method to be robust to model generalizations. We also present an example involving four communities where we show the invalidity of a plausible candidate variational formula for this limit.
title Critical point representation of the mutual information in the sparse stochastic block model
topic Probability
Information Theory
url https://arxiv.org/abs/2406.15233