For an Erdős-Rényi graph with fixed edge probability, the random graph Laplacian has simple spectrum with overwhelmingly high probability, with a quantitative n^{-3/2-o(1)} lower bound on the minimum gap.
Extreme eigenvalues of Laplacian random matrices with Gaussian entries
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abstract
A Laplacian matrix is a real symmetric matrix whose row and column sums are zero. We investigate the limiting distribution of the largest eigenvalues of a Laplacian random matrix with Gaussian entries. Unlike many classical matrix ensembles, this random matrix model contains dependent entries. Our main results show that the extreme eigenvalues of this model exhibit Poisson statistics. In particular, after properly shifting and scaling, we show that the largest eigenvalue converges to the Gumbel distribution as the dimension of the matrix tends to infinity. While the largest diagonal entry is also shown to have Gumbel fluctuations, there is a rather surprising difference between its deterministic centering term and the centering term required for the largest eigenvalues.
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Eigenvalue gaps of the Laplacian of random graphs
For an Erdős-Rényi graph with fixed edge probability, the random graph Laplacian has simple spectrum with overwhelmingly high probability, with a quantitative n^{-3/2-o(1)} lower bound on the minimum gap.