随机 d 规则图谱,直至边缘

IF 4.3 3区 材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC ACS Applied Electronic Materials Pub Date : 2023-09-28 DOI:10.1002/cpa.22176
Jiaoyang Huang, Horng-Tzer Yau
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(ii) All eigenvectors of random <i>d</i>-regular graphs are completely delocalized.</p>","PeriodicalId":3,"journal":{"name":"ACS Applied Electronic Materials","volume":null,"pages":null},"PeriodicalIF":4.3000,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Spectrum of random d-regular graphs up to the edge\",\"authors\":\"Jiaoyang Huang,&nbsp;Horng-Tzer Yau\",\"doi\":\"10.1002/cpa.22176\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Consider the normalized adjacency matrices of random <i>d</i>-regular graphs on <i>N</i> vertices with fixed degree <math>\\n <semantics>\\n <mrow>\\n <mi>d</mi>\\n <mo>⩾</mo>\\n <mn>3</mn>\\n </mrow>\\n <annotation>$d\\\\geqslant 3$</annotation>\\n </semantics></math>. 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引用次数: 0

摘要

考虑 N 个顶点上具有固定度 d ⩾ 3 $d\geqslant 3$ 的随机 d-regular 图的归一化邻接矩阵。我们证明,对于任意 ε > 0 $\varepsilon >0$ ,只要 d ⩾ 3 $d\geqslant 3$,以下两个性质在 N → ∞ $N \rightarrow \infty$ 时成立:(i) 特征值接近凯斯顿-麦凯分布给出的经典特征值位置。特别是,极值特征值以 N 的多项式误差约束集中,即 λ 2 , | λ N | | ⩽ 2 + N - c $\lambda _2, |\lambda _N|leqslant 2+N^{-c}$ 。 (ii) 随机 d-regular 图形的所有特征向量都是完全非局部化的。
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Spectrum of random d-regular graphs up to the edge

Consider the normalized adjacency matrices of random d-regular graphs on N vertices with fixed degree d 3 $d\geqslant 3$ . We prove that, with probability 1 N 1 + ε $1-N^{-1+\varepsilon }$ for any ε > 0 $\varepsilon >0$ , the following two properties hold as N $N \rightarrow \infty$ provided that d 3 $d\geqslant 3$ : (i) The eigenvalues are close to the classical eigenvalue locations given by the Kesten–McKay distribution. In particular, the extremal eigenvalues are concentrated with polynomial error bound in N, that is, λ 2 , | λ N | 2 + N c $\lambda _2, |\lambda _N|\leqslant 2+N^{-c}$ . (ii) All eigenvectors of random d-regular graphs are completely delocalized.

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7.20
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4.30%
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567
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