Functional limit theorems for random regular graphs

Abstract

Consider \(d\) uniformly random permutation matrices on \(n\) labels. Consider the sum of these matrices along with their transposes. The total can be interpreted as the adjacency matrix of a random regular graph of degree \(2d\) on \(n\) vertices. We consider limit theorems for various combinatorial and analytical properties of this graph (or the matrix) as \(n\) grows to infinity, either when \(d\) is kept fixed or grows slowly with \(n\). In a suitable weak convergence framework, we prove that the (finite but growing in length) sequences of the number of short cycles and of cyclically non-backtracking walks converge to distributional limits. We estimate the total variation distance from the limit using Stein’s method. As an application of these results we derive limits of linear functionals of the eigenvalues of the adjacency matrix. A key step in this latter derivation is an extension of the Kahn–Szemerédi argument for estimating the second largest eigenvalue for all values of \(d\) and \(n\).