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<p>Sharp conditions for the presence of spectral outliers are well understood for Wigner random matrices with independent and identically distributed entries. In the setting of <i>inhomogeneous</i> symmetric random matrices (i.e., matrices with a non-trivial variance profile), the corresponding problem has been considered only recently. Of special interest is the setting of sparse inhomogeneous matrices since sparsity is both a key feature and a technical obstacle in various aspects of random matrix theory. For such matrices, the largest of the variances of the entries has been used in the literature as a natural proxy for sparsity. We contribute sharp conditions in terms of this parameter for an inhomogeneous symmetric matrix with sub-Gaussian entries to have outliers. Our result implies a “structural” universality principle: The presence of outliers is only determined by the level of sparsity, rather than the detailed structure of the variance profile.</p>

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On Spectral Outliers of Inhomogeneous Symmetric Random Matrices

  • 期刊论文

  • 发表日期：

    2026年5月11日

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  • Dylan J. Altschuler,
  • Patrick Oliveira Santos,
  • Konstantin Tikhomirov,
  • Pierre Youssef

摘要

Sharp conditions for the presence of spectral outliers are well understood for Wigner random matrices with independent and identically distributed entries. In the setting of inhomogeneous symmetric random matrices (i.e., matrices with a non-trivial variance profile), the corresponding problem has been considered only recently. Of special interest is the setting of sparse inhomogeneous matrices since sparsity is both a key feature and a technical obstacle in various aspects of random matrix theory. For such matrices, the largest of the variances of the entries has been used in the literature as a natural proxy for sparsity. We contribute sharp conditions in terms of this parameter for an inhomogeneous symmetric matrix with sub-Gaussian entries to have outliers. Our result implies a “structural” universality principle: The presence of outliers is only determined by the level of sparsity, rather than the detailed structure of the variance profile.