Eigenvalues outside the bulk of inhomogeneous Erdos-R\"enyi random graphs
Abstract
The article considers an inhomogeneous Erdos-R\"enyi random graph on \1,…, N\, where an edge is placed between vertices i and j with probability N f(i/N,j/N), for i j, the choice being made independent for each pair. The function f is assumed to be non-negative definite, symmetric, bounded and of finite rank k. We study the edge of the spectrum of the adjacency matrix of such an inhomogeneous Erdos-R\'enyi random graph under the assumption that NN ∞ sufficiently fast. Although the bulk of the spectrum of the adjacency matrix, scaled by NN, is compactly supported, the k-th largest eigenvalue goes to infinity. It turns out that the largest eigenvalue after appropriate scaling and centering converge to a Gaussian law, if the largest eigenvalue of f has multiplicity 1. If f has k distinct non-zero eigenvalues, then the joint distribution of the k largest eigenvalues converge jointly to a multivariate Gaussian law. The first order behaviour of the eigenvectors is derived as a by-product of the above results. The results complement the homogeneous case derived by Erdos et al.(2013).
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