Color-avoiding percolation and branching processes

IF 0.7 4区 数学 Q3 STATISTICS & PROBABILITY Journal of Applied Probability Pub Date : 2024-03-08 DOI:10.1017/jpr.2023.115
Panna Tímea Fekete, Roland Molontay, Balázs Ráth, Kitti Varga
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Abstract

We study a variant of the color-avoiding percolation model introduced by Krause et al., namely we investigate the color-avoiding bond percolation setup on (not necessarily properly) edge-colored Erdős–Rényi random graphs. We say that two vertices are color-avoiding connected in an edge-colored graph if, after the removal of the edges of any color, they are in the same component in the remaining graph. The color-avoiding connected components of an edge-colored graph are maximal sets of vertices such that any two of them are color-avoiding connected. We consider the fraction of vertices contained in color-avoiding connected components of a given size, as well as the fraction of vertices contained in the giant color-avoidin g connected component. It is known that these quantities converge, and the limits can be expressed in terms of probabilities associated to edge-colored branching process trees. We provide explicit formulas for the limit of the fraction of vertices contained in the giant color-avoiding connected component, and we give a simpler asymptotic expression for it in the barely supercritical regime. In addition, in the two-colored case we also provide explicit formulas for the limit of the fraction of vertices contained in color-avoiding connected components of a given size.
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避色渗流和分支过程
我们研究的是克劳斯等人提出的避色渗滤模型的一个变体,即我们研究的是边缘着色的厄尔多斯-雷尼随机图(不一定是正确的)上的避色键渗滤设置。如果去掉任何颜色的边后,两个顶点在剩余图中处于同一分量中,我们就说这两个顶点在边色图中是避色相连的。边缘着色图中的避色连接成分是最大的顶点集合,其中任意两个顶点都是避色连接的。我们考虑的是特定大小的避色连通成分中包含的顶点的分数,以及巨型避色连通成分中包含的顶点的分数。众所周知,这些量是收敛的,极限可以用与边缘着色分支过程树相关的概率来表示。我们为巨型避色连通分量中包含的顶点分数的极限提供了明确的公式,并给出了在勉强超临界情况下的更简单的渐近表达式。此外,在双色情况下,我们还提供了给定大小的避色连通成分所含顶点分数的极限的明确公式。
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来源期刊
Journal of Applied Probability
Journal of Applied Probability 数学-统计学与概率论
CiteScore
1.50
自引率
10.00%
发文量
92
审稿时长
6-12 weeks
期刊介绍: Journal of Applied Probability is the oldest journal devoted to the publication of research in the field of applied probability. It is an international journal published by the Applied Probability Trust, and it serves as a companion publication to the Advances in Applied Probability. Its wide audience includes leading researchers across the entire spectrum of applied probability, including biosciences applications, operations research, telecommunications, computer science, engineering, epidemiology, financial mathematics, the physical and social sciences, and any field where stochastic modeling is used. A submission to Applied Probability represents a submission that may, at the Editor-in-Chief’s discretion, appear in either the Journal of Applied Probability or the Advances in Applied Probability. Typically, shorter papers appear in the Journal, with longer contributions appearing in the Advances.
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