Identifiability for graphexes and the weak kernel metric

TL;DR

This paper introduces a new metric for graphexes, extending the cut metric, and proves convergence, regularity, and identifiability results for sparse random graph processes based on graphexes.

Contribution

It generalizes the cut metric to graphexes, establishes convergence criteria, and provides an identifiability theorem for sparse graph processes.

Findings

Graphexes converge in the new metric iff their generated graph processes converge in distribution.

A regularity lemma for graphexes is established.

Identifiability conditions for equivalent graphexes are characterized.

Abstract

In two recent papers by Veitch and Roy and by Borgs, Chayes, Cohn, and Holden, a new class of sparse random graph processes based on the concept of graphexes over $\sigma$-finite measure spaces has been introduced. In this paper, we introduce a metric for graphexes that generalizes the cut metric for the graphons of the dense theory of graph convergence. We show that a sequence of graphexes converges in this metric if and only if the sequence of graph processes generated by the graphexes converges in distribution. In the course of the proof, we establish a regularity lemma and determine which sets of graphexes are precompact under our metric. Finally, we establish an identifiability theorem, characterizing when two graphexes are equivalent in the sense that they lead to the same process of random graphs.