Rare event asymptotics for exploration processes for random graphs
Shankar Bhamidi, Amarjit Budhiraja, Paul Dupuis, Ruoyu Wu

TL;DR
This paper develops a large deviation framework for exploring rare events in sparse random graphs, specifically the configuration model, by establishing LDPs for exploration processes and analyzing associated variational problems.
Contribution
It introduces a novel approach using exploration processes and infinite-dimensional LDPs to analyze rare events in sparse random graphs, extending beyond dense graph methods.
Findings
Established LDP for exploration process in the configuration model.
Derived explicit decay rate formulas for non-typical component degree distributions.
Analyzed Euler-Lagrange equations to solve the variational problems explicitly.
Abstract
Much work in the study of large deviations for random graph models is focused on the dense regime where the theory of graphons has emerged as a principal tool. These tools do not give a good approach to large deviation problems for random graph models in the sparse regime. The aim of this paper is to study an approach for large deviation problems in this regime by establishing Large Deviation Principles (LDP) on suitable path spaces for certain exploration processes of the associated random graph sequence. Our work focuses on the study of one particular class of random graph models, namely the configuration model; however the general approach of using exploration processes for studying large deviation properties of sparse random graph models has broader applicability. The goal is to study asymptotics of probabilities of non-typical behavior in the large network limit. The first key…
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Taxonomy
TopicsStochastic processes and statistical mechanics · Markov Chains and Monte Carlo Methods · Probability and Risk Models
