Second largest eigenpair statistics for sparse graphs

Abstract

We develop a formalism to compute the statistics of the second largest eigenpair of weighted sparse graphs with N ≫ 1 nodes, finite mean connectivity and bounded maximal degree, in cases where the top eigenpair statistics is known. The problem can be cast in terms of optimisation of a quadratic form on the sphere with a fictitious temperature, after a suitable deflation of the original matrix model. We use the cavity and replica methods to find the solution in terms of self-consistent equations for auxiliary probability density functions, which can be solved by an improved population dynamics algorithm enforcing eigenvector orthogonality on-the-fly. The analytical results are in perfect agreement with numerical diagonalisation of large (weighted) adjacency matrices, focussing on the cases of random regular and Erdős–Rényi (ER) graphs. We further analyse the case of sparse Markov transition matrices for unbiased random walks, whose second largest eigenpair describes the non-equilibrium mode with the largest relaxation time. We also show that the population dynamics algorithm with population size N P does not actually capture the thermodynamic limit N → ∞ as commonly assumed: the accuracy of the population dynamics algorithm has a strongly non-monotonic behaviour as a function of N P, thus implying that an optimal size must be chosen to best reproduce the results from numerical diagonalisation of graphs of finite size N.