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Extremal eigenvectors of sparse random matrices

Yukun He et al.

Probability Theory and Related Fields2026https://doi.org/10.1007/s00440-026-01472-2article
ABDC A*
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0.50

What the paper says

We consider a class of sparse random matrices, which includes the adjacency matrix of the Erdős-Rényi graph $$\textbf{G}(N,p)$$ G ( N , p ) . For $$N^{-1+o(1)}\leqslant p\leqslant 1/2$$ 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.

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https://doi.org/https://doi.org/10.1007/s00440-026-01472-2

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@article{yukun2026,
  title        = {{Extremal eigenvectors of sparse random matrices}},
  author       = {Yukun He et al.},
  journal      = {Probability Theory and Related Fields},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1007/s00440-026-01472-2},
}

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Extremal eigenvectors of sparse random matrices

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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