Large deviation principles for graphon sampling

Fuente: arXiv
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Main Authors: Grebík, Jan, Pikhurko, Oleg
Format: Preprint
Published: 2023
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author Grebík, Jan
Pikhurko, Oleg
author_facet Grebík, Jan
Pikhurko, Oleg
contents We investigate possible large deviation principles (LDPs) for the $n$-vertex sampling from a given graphon with various speeds $s(n)$ and resolve all the cases except when the speed $s(n)$ is of order $n^2$. For quadratic speed $s=(c+o(1))n^2$, we establish an LDP for an arbitrary $k$-step graphon, which extends a result of Chatterjee and Varadhan [Europ. J. Combin., 32 (2011) 1000-1017] who did this for $k=1$ (that is, for the homogeneous binomial random graphs). This is done by reducing the problem to the LDP for stochastic $k$-block models established recently by Borgs, Chayes, Gaudio, Petti and Sen ["A large deviation principle for block models", arxiv:2007.14508, 2020]. Also, we improve some results by Borgs et al.
format Preprint
id arxiv_https___arxiv_org_abs_2311_06531
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle Large deviation principles for graphon sampling
Grebík, Jan
Pikhurko, Oleg
Probability
Combinatorics
We investigate possible large deviation principles (LDPs) for the $n$-vertex sampling from a given graphon with various speeds $s(n)$ and resolve all the cases except when the speed $s(n)$ is of order $n^2$. For quadratic speed $s=(c+o(1))n^2$, we establish an LDP for an arbitrary $k$-step graphon, which extends a result of Chatterjee and Varadhan [Europ. J. Combin., 32 (2011) 1000-1017] who did this for $k=1$ (that is, for the homogeneous binomial random graphs). This is done by reducing the problem to the LDP for stochastic $k$-block models established recently by Borgs, Chayes, Gaudio, Petti and Sen ["A large deviation principle for block models", arxiv:2007.14508, 2020]. Also, we improve some results by Borgs et al.
title Large deviation principles for graphon sampling
topic Probability
Combinatorics
url https://arxiv.org/abs/2311.06531