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Main Author: Lienau, Matthias
Format: Preprint
Published: 2024
Subjects:
Online Access:https://arxiv.org/abs/2405.08708
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author Lienau, Matthias
author_facet Lienau, Matthias
contents The generalised random graph contains $n$ vertices with positive i.i.d. weights. The probability of adding an edge between two vertices is increasing in their weights. We require the weight distribution to have finite second moments and study the point process $\mathcal{C}_n$ on $\{3,4,\dots\}$, which counts how many cycles of the respective length are present in the graph. We establish convergence of $\mathcal{C}_n$ to a Poisson point process. Under the stronger assumption of the weights having finite fourth moments we provide the following results. When $\mathcal{C}_n$ is evaluated on a bounded set $A$, we provide a rate of convergence. If the graph is additionally subcritical, we extend this to unbounded sets $A$ at the cost of a slower rate of convergence. From this we deduce the limiting distribution of the length of the shortest and the longest cycle when the graph is subcritical, including rates of convergence. All mentioned results also apply to the Chung-Lu model and the Norros-Reittu model.
format Preprint
id arxiv_https___arxiv_org_abs_2405_08708
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Poisson approximation for cycles in the generalised random graph
Lienau, Matthias
Probability
05C80, 60F05, 60G70
The generalised random graph contains $n$ vertices with positive i.i.d. weights. The probability of adding an edge between two vertices is increasing in their weights. We require the weight distribution to have finite second moments and study the point process $\mathcal{C}_n$ on $\{3,4,\dots\}$, which counts how many cycles of the respective length are present in the graph. We establish convergence of $\mathcal{C}_n$ to a Poisson point process. Under the stronger assumption of the weights having finite fourth moments we provide the following results. When $\mathcal{C}_n$ is evaluated on a bounded set $A$, we provide a rate of convergence. If the graph is additionally subcritical, we extend this to unbounded sets $A$ at the cost of a slower rate of convergence. From this we deduce the limiting distribution of the length of the shortest and the longest cycle when the graph is subcritical, including rates of convergence. All mentioned results also apply to the Chung-Lu model and the Norros-Reittu model.
title Poisson approximation for cycles in the generalised random graph
topic Probability
05C80, 60F05, 60G70
url https://arxiv.org/abs/2405.08708