Extremal eigenvectors of sparse random matrices

Fuente: arXiv
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Main Authors: He, Yukun, Huang, Jiaoyang, Wang, Chen
Format: Preprint
Published: 2025
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author He, Yukun
Huang, Jiaoyang
Wang, Chen
author_facet He, Yukun
Huang, Jiaoyang
Wang, Chen
contents We consider a class of sparse random matrices, which includes the adjacency matrix of Erdős-Rényi graph ${\bf G}(N,p)$. For $N^{-1+o(1)}\leq p\leq 1/2$, we show that the non-trivial edge eigenvectors are asymptotically jointly normal. The main ingredient of the proof is an algorithm that directly computes the joint eigenvector distributions, without comparisons with GOE. The method is applicable in general. As an illustration, we also use it to prove the normal fluctuation in quantum ergodicity at the edge for Wigner matrices. Another ingredient of the proof is the isotropic local law for sparse matrices, which at the same time improves several existing results.
format Preprint
id arxiv_https___arxiv_org_abs_2501_16444
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Extremal eigenvectors of sparse random matrices
He, Yukun
Huang, Jiaoyang
Wang, Chen
Probability
Mathematical Physics
05C80, 05C50, 60B20, 15B52
We consider a class of sparse random matrices, which includes the adjacency matrix of Erdős-Rényi graph ${\bf G}(N,p)$. For $N^{-1+o(1)}\leq p\leq 1/2$, we show that the non-trivial edge eigenvectors are asymptotically jointly normal. The main ingredient of the proof is an algorithm that directly computes the joint eigenvector distributions, without comparisons with GOE. The method is applicable in general. As an illustration, we also use it to prove the normal fluctuation in quantum ergodicity at the edge for Wigner matrices. Another ingredient of the proof is the isotropic local law for sparse matrices, which at the same time improves several existing results.
title Extremal eigenvectors of sparse random matrices
topic Probability
Mathematical Physics
05C80, 05C50, 60B20, 15B52
url https://arxiv.org/abs/2501.16444