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arXiv 1609.09052math.PRmath-phmath.COmath.MP
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随机正则图的McKay律

Local Kesten--McKay law for random regular graphs

Roland Bauerschmidt, Jiaoyang Huang, Horng-Tzer Yau

摘要:

本文研究了随机$d$ -正则图的邻接矩阵,其中$d$的度较大但固定。在大部分的频谱$[-2\sqrt{d-1}+\varepsilon, 2\sqrt{d-1}-\varepsilon]$下降到最佳的频谱尺度,我们证明了格林函数可以近似的某些无限树状(几个周期)图,只依赖于原始图形的局部结构。这一结果意味着Kesten-McKay定律适用于最小尺度的谱密度和体特征向量的完全离域。我们的方法是基于估计的格林函数的邻接矩阵和一个restriation的大球的边界边的图。

英文摘要:

We study the adjacency matrices of random $d$-regular graphs with large but fixed degree $d$. In the bulk of the spectrum $[-2\sqrt{d-1}+\varepsilon, 2\sqrt{d-1}-\varepsilon]$ down to the optimal spectral scale, we prove that the Green's functions can be approximated by those of certain infinite tree-like (few cycles) graphs that depend only on the local structure of the original graphs. This result implies that the Kesten--McKay law holds for the spectral density down to the smallest scale and the complete delocalization of bulk eigenvectors. Our method is based on estimating the Green's function of the adjacency matrices and a resampling of the boundary edges of large balls in the graphs.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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