Extremal eigenvectors of sparse random matrices
作者:
时间:2025-09-28
阅读量:138次
  • 演讲人: 何煜坤(复旦大学,副教授)
  • 时间:2025年10月10日14:00
  • 地点:浙江大学紫金港校区行政楼1417报告厅
  • 主办单位:浙江大学数据科学研究中心

Abstract: We consider a class of sparse random matrices, which includes the adjacency matrix of Erdős-Rényi graph. When $p\geq N^{-1+o(1)}$, we show that the non-trivial edge eigenvectors are asymptotically jointly normal. The main ingredient of the proof is a new algorithm that directly computes the joint eigenvector distributions, without comparisons with GOE. Joint work with Jiaoyang Huang and Chen Wang.

Bio:Yukun He is an associate profrssor at Shanghai Center for Mathematical Sciences, Fudan University. He obtained his PhD in university of Geneva, and has previously worked in University of Zurich and City University of Hong Kong. He works on random matrix theory, in particular the spectrum and eigenvectors of random graphs. He has published papers in Annals of Probability, Annals of Applied Probability, Probability Theory and Related Fields, and Communications in Mathematical Physics.