Asymptotic normality for triangle counting in the sparse $β$-model

Fuente: arXiv
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Hauptverfasser: Zhang, Siang, Feng, Qunqiang, Hu, Zhishui
Format: Preprint
Veröffentlicht: 2026
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author Zhang, Siang
Feng, Qunqiang
Hu, Zhishui
author_facet Zhang, Siang
Feng, Qunqiang
Hu, Zhishui
contents We study the number of triangles $T_n$ in the sparse $β$-model on $n$ vertices, a random graph model that captures degree heterogeneity in real-world networks. Using the norms of the heterogeneity parameter vector, we first determine the asymptotic mean and variance of $T_n$. Next, by applying the Malliavin-Stein method, we derive a non-asymptotic upper bound on the Kolmogorov distance between normalized $T_n$ and the standard normal distribution. Under an additional assumption on degree heterogeneity, we further prove the asymptotic normality for $T_n$, as $n\to\infty$.
format Preprint
id arxiv_https___arxiv_org_abs_2603_01395
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Asymptotic normality for triangle counting in the sparse $β$-model
Zhang, Siang
Feng, Qunqiang
Hu, Zhishui
Probability
We study the number of triangles $T_n$ in the sparse $β$-model on $n$ vertices, a random graph model that captures degree heterogeneity in real-world networks. Using the norms of the heterogeneity parameter vector, we first determine the asymptotic mean and variance of $T_n$. Next, by applying the Malliavin-Stein method, we derive a non-asymptotic upper bound on the Kolmogorov distance between normalized $T_n$ and the standard normal distribution. Under an additional assumption on degree heterogeneity, we further prove the asymptotic normality for $T_n$, as $n\to\infty$.
title Asymptotic normality for triangle counting in the sparse $β$-model
topic Probability
url https://arxiv.org/abs/2603.01395