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The Second-Moment Phenomenon for Monochromatic Subgraphs
SIAM Journal on Discrete Mathematics ( IF 0.9 ) Pub Date : 2020-03-23 , DOI: 10.1137/18m1184461
Bhaswar B. Bhattacharya , Somabha Mukherjee , Sumit Mukherjee

SIAM Journal on Discrete Mathematics, Volume 34, Issue 1, Page 794-824, January 2020.
What is the chance that among a group of $n$ friends, there are $s$ friends all of whom have the same birthday? This is the celebrated birthday problem which can be formulated as the existence of a monochromatic $s$-clique $K_s$ ($s$-matching birthdays) in the complete graph $K_n$, where every vertex of $K_n$ is uniformly colored with 365 colors (corresponding to birthdays). More generally, for a general connected graph $H$, let $T(H, G_n)$ be the number of monochromatic copies of $H$ in a uniformly random coloring of the vertices of the graph $G_n$ with $c_n$ colors. In this paper we show that $T(H, G_n)$ converges to ${Pois}(\lambda)$ whenever $\mathbb{E} T(H, G_n) \rightarrow \lambda$ and ${Var} T(H, G_n) \rightarrow \lambda$, that is, the asymptotic Poisson distribution of $T(H, G_n)$ is determined just by the convergence of its mean and variance. Moreover, this condition is necessary if and only if $H$ is a star-graph. In fact, the second-moment phenomenon is a consequence of a more general theorem about the convergence of $T(H,G_n)$ to a finite linear combination of independent Poisson random variables. As an application, we derive the limiting distribution of $T(H, G_n)$, when $G_n\sim G(n, p)$ is the Erdös--Rényi random graph. Multiple phase transitions emerge as $p$ varies from 0 to 1, depending on whether the graph $H$ is balanced or unbalanced.


中文翻译:

单色子图的第二矩现象

SIAM离散数学杂志,第34卷,第1期,第794-824页,2020年1月。
在一群$ n $朋友中,有$ s $朋友的生日都相同,这是什么机会?这是一个著名的生日问题,可以用完整的图形$ K_n $中存在单色的$ s $斜线$ K_s $($ s $匹配生日)的形式表示,其中$ K_n $的每个顶点都均匀地着色365种颜色(对应于生日)。更一般地,对于一般的连通图$ H $,令$ T(H,G_n)$为图形$ G_n $的顶点均匀随机着色为$ c_n $的颜色时,$ H $的单色副本的数量。 。在本文中,我们证明了只要$ \ mathbb {E} T(H,G_n)\ rightarrow \ lambda $和$ {Var} T($ {T(H,G_n)$}收敛到$ {Pois}(\ lambda)$ H,G_n)\ rightarrow \ lambda $,即$ T(H,的渐近泊松分布 G_n)$仅由其均值和方差的收敛确定。而且,仅当$ H $是星图时,此条件是必要的。实际上,第二矩现象是一个更普遍的定理的结果,该定理涉及将$ T(H,G_n)$收敛到独立泊松随机变量的有限线性组合。作为应用程序,当$ G_n \ sim G(n,p)$是Erdös-Rényi随机图时,我们导出$ T(H,G_n)$的极限分布。随着$ p $从0到1变化,出现多个相变,具体取决于图形$ H $是平衡的还是不平衡的。作为应用程序,当$ G_n \ sim G(n,p)$是Erdös-Rényi随机图时,我们导出$ T(H,G_n)$的极限分布。随着$ p $从0到1变化,出现多个相变,具体取决于图形$ H $是平衡的还是不平衡的。作为应用程序,当$ G_n \ sim G(n,p)$是Erdös-Rényi随机图时,我们导出$ T(H,G_n)$的极限分布。随着$ p $从0到1变化,出现多个相变,具体取决于图形$ H $是平衡的还是不平衡的。
更新日期:2020-03-23
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