Random matrices with row constraints and eigenvalue distributions of graph Laplacians

Abstract

AbstractSymmetric matrices with zero row sums occur in many theoretical settings and in real-life applications. When the offdiagonal elements of such matrices are i.i.d. random variables and the matrices are large, the eigenvalue distributions converge to a peculiar universal curvepzrs(λ)that looks like a cross between the Wigner semicircle and a Gaussian distribution. An analytic theory for this curve, originally due to Fyodorov, can be developed using supersymmetry-based techniques. We extend these derivations to the case of sparse matrices, including the important case of graph Laplacians for large random graphs withNvertices of mean degreec. In the regime1≪c≪N, the eigenvalue distribution of the ordinary graph Laplacian (diffusion with a fixed transition rate per edge) tends to a shifted and scaled version ofpzrs(λ), centered atcwith width∼c. At smallerc, this curve receives corrections in powers of1/caccurately captured by our theory. For the normalized graph Laplacian (diffusion with a fixed transition rate per vertex), the largeclimit is a shifted and scaled Wigner semicircle, again with corrections captured by our analysis.