Unusually large components in near-critical Erdős-Rényi graphs via ballot theorems

IF 0.9 4区 数学 Q3 COMPUTER SCIENCE, THEORY & METHODS Combinatorics, Probability & Computing Pub Date : 2021-01-13 DOI:10.1017/s0963548321000584
Umberto De Ambroggio, Matthew I. Roberts
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引用次数: 7

Abstract

We consider the near-critical Erdős–Rényi random graph G(n, p) and provide a new probabilistic proof of the fact that, when p is of the form $p=p(n)=1/n+\lambda/n^{4/3}$ and A is large, \begin{equation*}\mathbb{P}(|\mathcal{C}_{\max}|>An^{2/3})\asymp A^{-3/2}e^{-\frac{A^3}{8}+\frac{\lambda A^2}{2}-\frac{\lambda^2A}{2}},\end{equation*} where $\mathcal{C}_{\max}$ is the largest connected component of the graph. Our result allows A and $\lambda$ to depend on n. While this result is already known, our proof relies only on conceptual and adaptable tools such as ballot theorems, whereas the existing proof relies on a combinatorial formula specific to Erdős–Rényi graphs, together with analytic estimates.
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通过选票定理,在接近临界的Erdős-Rényi图中出现异常大的组件
我们考虑近临界Erdős-Rényi随机图G(n, p),并提供了一个新的概率证明,证明当p的形式为$p=p(n)=1/n+\lambda/n^{4/3}$且a很大时,\begin{equation*}\mathbb{P}(|\mathcal{C}_{\max}|>An^{2/3})\asymp A^{-3/2}e^{-\frac{A^3}{8}+\frac{\lambda A^2}{2}-\frac{\lambda^2A}{2}},\end{equation*}其中$\mathcal{C}_{\max}$是图的最大连接分量。我们的结果允许A和$\lambda$依赖于n。虽然这个结果是已知的,但我们的证明只依赖于概念性和适应性的工具,如选票定理,而现有的证明依赖于Erdős-Rényi图特定的组合公式,以及分析估计。
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来源期刊
Combinatorics, Probability & Computing
Combinatorics, Probability & Computing 数学-计算机:理论方法
CiteScore
2.40
自引率
11.10%
发文量
33
审稿时长
6-12 weeks
期刊介绍: Published bimonthly, Combinatorics, Probability & Computing is devoted to the three areas of combinatorics, probability theory and theoretical computer science. Topics covered include classical and algebraic graph theory, extremal set theory, matroid theory, probabilistic methods and random combinatorial structures; combinatorial probability and limit theorems for random combinatorial structures; the theory of algorithms (including complexity theory), randomised algorithms, probabilistic analysis of algorithms, computational learning theory and optimisation.
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