Phase Transitions and a Model Order Selection Criterion for Spectral Graph Clustering

Abstract

One of the longstanding open problems in spectral graph clustering (SGC) is the so-called model order selection problem: automated selection of the correct number of clusters. This is equivalent to the problem of finding the number of connected components or communities in an undirected graph. We propose an automated model order selection (AMOS), a solution to the SGC model selection problem under a random interconnection model using a novel selection criterion that is based on an asymptotic phase transition analysis. AMOS can more generally be applied to discovering hidden block diagonal structure in symmetric non-negative matrices. Numerical experiments on simulated graphs validate the phase transition analysis, and real-world network data are used to validate the performance of the proposed model selection procedure.