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Edge universality of sparse Erd\H{o}s-R\'enyi digraphs

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arxiv 2304.04723 v6 pith:URXZO3XD submitted 2023-04-10 math.PR math-phmath.MP

classification math.PRmath-phmath.MP
keywords lambdamathcaledgerandomsparsestatisticsbigghermitian
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abstract

Let $\mathcal A$ be the adjacency matrix of the Erd\H{o}s-R\'{e}nyi directed graph $\mathscr G(N,p)$. We denote the eigenvalues of $\mathcal A$ by $\lambda_1^{\cal A},...,\lambda^{\cal A}_N$, and $|\lambda_1^{\cal A}|=\max_i|\lambda_i^{\cal A}|$. For $N^{-1+o(1)}\leq p\leq 1/2$, we show that \[ \max_{i=2,3,...,N} \bigg|\frac{\lambda_i^{\mathcal A}}{\sqrt{Np(1-p)}}\bigg| =1+O(N^{-1/2+o(1)}) \] with very high probability. In addition, we prove that near the unit circle, the local eigenvalue statistics of ${\mathcal A}/\sqrt{Np(1-p)}$ coincide with those of the real Ginibre ensemble. As a by-product, we also show that all non-trivial eigenvectors of $\mathcal A$ are completely delocalized. For Hermitian random matrices, it is known that the edge statistics are sensitive to the sparsity: in the very sparse regime, one needs to remove many noise random variables (which affect both the mean and the fluctuation) to recover the Tracy-Widom distribution. Our results imply that, compared to their analogues in the Hermitian case, the edge statistics of non-Hermitian sparse random matrices are more robust.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Bulk Universality for Sparse Complex non-Hermitian Random Matrices

    math.PR 2025-08 conditional novelty 7.0 of 10

    Bulk local eigenvalue statistics of sparse complex non-Hermitian random matrices are universal and match the complex Ginibre ensemble.

  2. Spectral radius concentration for inhomogeneous random matrices with independent entries

    math.PR 2025-01 conditional novelty 7.0 of 10

    For inhomogeneous random matrices, the spectral radius is bounded by the variance row/column sums up to the optimal sparsity (log n)^{-1/2}.

  3. Matrix Completion via Residual Spectral Matching

    stat.ML 2024-12 reject novelty 7.0 of 10

    A residual spectral matching estimator for noisy matrix completion matches the singular values of residuals to those of sparse random matrices and claims minimax optimal error rates.

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