Normal approximation for subgraph counts in age-dependent random connection models

We study normal approximation of subgraph counts in a model of spatial scale-free random networks known as the age-dependent random connection model. In the light-tailed regime where only moments of order $(2 + \varepsilon)$ are finite, we study the asymptotic normality of both clique and subtree counts. For clique counts, we establish a multivariate quantitative normal approximation result through the Malliavin-Stein method. In the more delicate case of subtree counts, we obtain distributional convergence based on a central limit theorem for sequences of associated random variables.

Publication Details

Journal
ArXiv.org
Published
2025-05-14
Primary Topic
Random Matrices and Applications
Type
article
Field-Weighted Citation Impact
0.00

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article

Normal approximation for subgraph counts in age-dependent random connection models

ArXiv.org
Random Matrices and Applications
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Normal approximation for subgraph counts in age-dependent random connection models

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Abstract

We study normal approximation of subgraph counts in a model of spatial scale-free random networks known as the age-dependent random connection model. In the light-tailed regime where only moments of order $(2 + \varepsilon)$ are finite, we study the asymptotic normality of both clique and subtree counts. For clique counts, we establish a multivariate quantitative normal approximation result through the Malliavin-Stein method. In the more delicate case of subtree counts, we obtain distributional convergence based on a central limit theorem for sequences of associated random variables.

ArXiv.org
Purdue University, Aarhus Universitet, Villum Fonden, Air Force Office of Scientific Research
Reduced inequalities
Openalex Percentile: Top 46%
Random Matrices and Applications
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Normal approximation for subgraph counts in age-dependent random connection models · ArXiv.org (2025) | TGRS Research Map | TGRS