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Extremal eigenvectors of sparse random matrices

  • Yukun He
  • , Jiaoyang Huang*
  • , Chen Wang
  • *Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

Abstract

We consider a class of sparse random matrices, which includes the adjacency matrix of the Erdős-Rényi graph G(N, p). For N−1+o(1)p ⩽ 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. © The Author(s) 2026.
Original languageEnglish
Number of pages58
JournalProbability Theory and Related Fields
Online published6 Mar 2026
DOIs
Publication statusOnline published - 6 Mar 2026

Funding

YH and CW are supported by National Key R&D Program of China No. pg 2023YFA1010400, NSFC No. 12322121 and Hong Kong RGC Grant No. 21300223. The research of JH is supported by NSF grants DMS-2331096 and DMS-2337795, and the Sloan Research Award. The authors thank the reviewers for their careful reading of the manuscript and for their helpful comments.

RGC Funding Information

  • RGC-funded

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