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1. Verfasser: Feng, Chenxu
Format: Preprint
Veröffentlicht: 2025
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Online-Zugang:https://arxiv.org/abs/2506.12752
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author Feng, Chenxu
author_facet Feng, Chenxu
contents Consider a pair of correlated Erdős-Rényi graphs $\mathcal G(n,\tfracλ{n};s)$ that are subsampled from a common parent Erdős-Rényi graph with average degree $λ$ and subsampling probability $s$. We establish a sharp information-theoretic threshold for the detection problem between this model and two independent Erdős-Rényi graphs $\mathcal G(n,\tfracλ{n})$, showing that strong detection is information-theoretically possible if and only if $s>\min\{ \tfrac{1}{\sqrtλ}, \sqrtα \}$ where $α\approx 0.338$ is the Otter's constant. Our result resolves a constant gap between arXiv:2203.14573 and arXiv:2008.10097.
format Preprint
id arxiv_https___arxiv_org_abs_2506_12752
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Strong Detection Threshold for Correlated Erdős-Rényi Graphs with Constant Average Degree
Feng, Chenxu
Probability
05C80
Consider a pair of correlated Erdős-Rényi graphs $\mathcal G(n,\tfracλ{n};s)$ that are subsampled from a common parent Erdős-Rényi graph with average degree $λ$ and subsampling probability $s$. We establish a sharp information-theoretic threshold for the detection problem between this model and two independent Erdős-Rényi graphs $\mathcal G(n,\tfracλ{n})$, showing that strong detection is information-theoretically possible if and only if $s>\min\{ \tfrac{1}{\sqrtλ}, \sqrtα \}$ where $α\approx 0.338$ is the Otter's constant. Our result resolves a constant gap between arXiv:2203.14573 and arXiv:2008.10097.
title Strong Detection Threshold for Correlated Erdős-Rényi Graphs with Constant Average Degree
topic Probability
05C80
url https://arxiv.org/abs/2506.12752