Spectrum of Lévy–Khintchine Random Laplacian Matrices

Andrew Campbell () and Sean O’Rourke ()
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Andrew Campbell: Institute of Science and Technology Austria
Sean O’Rourke: University of Colorado at Boulder

Journal of Theoretical Probability, 2024, vol. 37, issue 1, 933-973

Abstract: Abstract We consider the spectrum of random Laplacian matrices of the form $$L_n=A_n-D_n$$ L n = A n - D n where $$A_n$$ A n is a real symmetric random matrix and $$D_n$$ D n is a diagonal matrix whose entries are equal to the corresponding row sums of $$A_n$$ A n . If $$A_n$$ A n is a Wigner matrix with entries in the domain of attraction of a Gaussian distribution, the empirical spectral measure of $$L_n$$ L n is known to converge to the free convolution of a semicircle distribution and a standard real Gaussian distribution. We consider real symmetric random matrices $$A_n$$ A n with independent entries (up to symmetry) whose row sums converge to a purely non-Gaussian infinitely divisible distribution, which fall into the class of Lévy–Khintchine random matrices first introduced by Jung [Trans Am Math Soc, 370, (2018)]. Our main result shows that the empirical spectral measure of $$L_n$$ L n converges almost surely to a deterministic limit. A key step in the proof is to use the purely non-Gaussian nature of the row sums to build a random operator to which $$L_n$$ L n converges in an appropriate sense. This operator leads to a recursive distributional equation uniquely describing the Stieltjes transform of the limiting empirical spectral measure.

Keywords: Symmetric random matrices; Poisson point process; $$\alpha $$ α -Stable laws; Infinitely divisible laws; Heavy-tailed entries; Random Laplacian matrices; Empirical spectral measure; Primary:; 60B20 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10959-023-01275-4

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