Central limit theorem for the principal eigenvalue and eigenvector of Chung-Lu random graphs

Abstract

A Chung-Lu random graph is an inhomogeneous Erdős-Rényi random graph in which vertices are assigned average degrees, and pairs of vertices are connected by an edge with a probability that is proportional to the product of their average degrees, independently for different edges. We derive a central limit theorem for the principal eigenvalue and the components of the principal eigenvector of the adjacency matrix of a Chung-Lu random graph. Our derivation requires certain assumptions on the average degrees that guarantee connectivity, sparsity and bounded inhomogeneity of the graph.

References in corpus (2)

• Finding community structure in networks using the eigenvectors of matrices
• Central limit theorems for eigenvalues in a spiked population model

Cited by in corpus (1)

• Spectral signatures of structural change in financial networks