厄尔多斯-雷尼图中命中时间的浓度

Pub Date : 2024-05-12 DOI:10.1002/jgt.23119
Andrea Ottolini, Stefan Steinerberger
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A natural guess is that an Erdős-Rényi random graph is so homogeneous that it does not really distinguish between vertices and <span></span><math>\n <semantics>\n <mrow>\n <msub>\n <mi>H</mi>\n <mrow>\n <mi>w</mi>\n <mi>v</mi>\n </mrow>\n </msub>\n <mo>=</mo>\n <mrow>\n <mo>(</mo>\n <mrow>\n <mn>1</mn>\n <mo>+</mo>\n <mi>o</mi>\n <mrow>\n <mo>(</mo>\n <mn>1</mn>\n <mo>)</mo>\n </mrow>\n </mrow>\n <mo>)</mo>\n </mrow>\n <mi>n</mi>\n </mrow>\n <annotation> ${H}_{wv}=(1+o(1))n$</annotation>\n </semantics></math>. Löwe-Terveer established a CLT for the Mean Starting Hitting Time suggesting <span></span><math>\n <semantics>\n <mrow>\n <msub>\n <mi>H</mi>\n <mrow>\n <mi>w</mi>\n <mi>v</mi>\n </mrow>\n </msub>\n <mo>=</mo>\n <mi>n</mi>\n <mo>±</mo>\n <mi>O</mi>\n <mrow>\n <mo>(</mo>\n <msqrt>\n <mi>n</mi>\n </msqrt>\n <mo>)</mo>\n </mrow>\n </mrow>\n <annotation> ${H}_{wv}=n\\pm {\\mathscr{O}}(\\sqrt{n})$</annotation>\n </semantics></math>. We prove the existence of a strong concentration phenomenon: <span></span><math>\n <semantics>\n <mrow>\n <msub>\n <mi>H</mi>\n <mrow>\n <mi>w</mi>\n <mi>v</mi>\n </mrow>\n </msub>\n </mrow>\n <annotation> ${H}_{wv}$</annotation>\n </semantics></math> is given, up to a very small error of size <span></span><math>\n <semantics>\n <mrow>\n <mo>≲</mo>\n <msup>\n <mrow>\n <mo>(</mo>\n <mrow>\n <mi>log</mi>\n <mi>n</mi>\n </mrow>\n <mo>)</mo>\n </mrow>\n <mrow>\n <mn>3</mn>\n <mo>∕</mo>\n <mn>2</mn>\n </mrow>\n </msup>\n <mo>∕</mo>\n <msqrt>\n <mi>n</mi>\n </msqrt>\n </mrow>\n <annotation> $\\lesssim {(\\mathrm{log}n)}^{3\\unicode{x02215}2}\\unicode{x02215}\\sqrt{n}$</annotation>\n </semantics></math>, by an explicit simple formula involving only the total number of edges <span></span><math>\n <semantics>\n <mrow>\n <mo>∣</mo>\n <mi>E</mi>\n <mo>∣</mo>\n </mrow>\n <annotation> $| E| $</annotation>\n </semantics></math>, the degree <span></span><math>\n <semantics>\n <mrow>\n <mtext>deg</mtext>\n <mrow>\n <mo>(</mo>\n <mi>v</mi>\n <mo>)</mo>\n </mrow>\n </mrow>\n <annotation> $\\text{deg}(v)$</annotation>\n </semantics></math> and the distance <span></span><math>\n <semantics>\n <mrow>\n <mi>d</mi>\n <mrow>\n <mo>(</mo>\n <mrow>\n <mi>v</mi>\n <mo>,</mo>\n <mi>w</mi>\n </mrow>\n <mo>)</mo>\n </mrow>\n </mrow>\n <annotation> $d(v,w)$</annotation>\n </semantics></math>.</p>","PeriodicalId":0,"journal":{"name":"","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Concentration of hitting times in Erdős-Rényi graphs\",\"authors\":\"Andrea Ottolini,&nbsp;Stefan Steinerberger\",\"doi\":\"10.1002/jgt.23119\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>We consider Erdős-Rényi graphs <span></span><math>\\n <semantics>\\n <mrow>\\n <mi>G</mi>\\n <mrow>\\n <mo>(</mo>\\n <mrow>\\n <mi>n</mi>\\n <mo>,</mo>\\n <mi>p</mi>\\n </mrow>\\n <mo>)</mo>\\n </mrow>\\n </mrow>\\n <annotation> $G(n,p)$</annotation>\\n </semantics></math> for <span></span><math>\\n <semantics>\\n <mrow>\\n <mn>0</mn>\\n <mo>&lt;</mo>\\n <mi>p</mi>\\n <mo>&lt;</mo>\\n <mn>1</mn>\\n </mrow>\\n <annotation> $0\\\\lt p\\\\lt 1$</annotation>\\n </semantics></math> fixed and <span></span><math>\\n <semantics>\\n <mrow>\\n <mi>n</mi>\\n <mo>→</mo>\\n <mi>∞</mi>\\n </mrow>\\n <annotation> $n\\\\to \\\\infty $</annotation>\\n </semantics></math> and study the expected number of steps, <span></span><math>\\n <semantics>\\n <mrow>\\n <msub>\\n <mi>H</mi>\\n <mrow>\\n <mi>w</mi>\\n <mi>v</mi>\\n </mrow>\\n </msub>\\n </mrow>\\n <annotation> ${H}_{wv}$</annotation>\\n </semantics></math>, that a random walk started in <span></span><math>\\n <semantics>\\n <mrow>\\n <mi>w</mi>\\n </mrow>\\n <annotation> $w$</annotation>\\n </semantics></math> needs to first arrive in <span></span><math>\\n <semantics>\\n <mrow>\\n <mi>v</mi>\\n </mrow>\\n <annotation> $v$</annotation>\\n </semantics></math>. 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引用次数: 0

摘要

我们考虑了固定的 和 的厄尔多斯-雷尼图,并研究了从 开始的随机漫步到达 。一个自然的猜测是,Erdős-Rényi 随机图是如此同质,以至于它并不真正区分顶点 和 。Löwe-Terveer 建立了平均起始击球时间的 CLT,表明 .我们证明了一种强集中现象的存在:它是由一个只涉及边的总数、度和距离的显式简单公式给出的,误差很小。
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Concentration of hitting times in Erdős-Rényi graphs

We consider Erdős-Rényi graphs G ( n , p ) $G(n,p)$ for 0 < p < 1 $0\lt p\lt 1$ fixed and n $n\to \infty $ and study the expected number of steps, H w v ${H}_{wv}$ , that a random walk started in w $w$ needs to first arrive in v $v$ . A natural guess is that an Erdős-Rényi random graph is so homogeneous that it does not really distinguish between vertices and H w v = ( 1 + o ( 1 ) ) n ${H}_{wv}=(1+o(1))n$ . Löwe-Terveer established a CLT for the Mean Starting Hitting Time suggesting H w v = n ± O ( n ) ${H}_{wv}=n\pm {\mathscr{O}}(\sqrt{n})$ . We prove the existence of a strong concentration phenomenon: H w v ${H}_{wv}$ is given, up to a very small error of size ( log n ) 3 2 n $\lesssim {(\mathrm{log}n)}^{3\unicode{x02215}2}\unicode{x02215}\sqrt{n}$ , by an explicit simple formula involving only the total number of edges E $| E| $ , the degree deg ( v ) $\text{deg}(v)$ and the distance d ( v , w ) $d(v,w)$ .

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