Graph alignment in sparse inhomogeneous models via self-overlap

Abstract

We develop a general framework for understanding when graph alignment is information-theoretically feasible in sparse inhomogeneous random graph models, by studying the set of vertices on which the underlying matching can be recovered. Our main theorem gives a general lower bound on this set by leveraging the balanced load function introduced by Hajek (1990). The corresponding obstruction is captured by a new graph parameter, the self-overlap, which measures the extent to which a graph can imitate itself under a non-trivial relabelling. We then show that this criterion is sharp in a broad class of sparse inhomogeneous models, recovering known Erd\H{o}s--R\'enyi phenomena and yielding sharp thresholds for Chung--Lu graphs and stochastic block models.