Typical distances in the directed configuration model

TL;DR

This paper studies the typical distances in directed graphs generated by the configuration model, revealing logarithmic growth under certain conditions and introducing a novel coupling method for analysis.

Contribution

It introduces a new coupling technique between graph exploration and branching processes, extending analysis capabilities for directed random graphs.

Findings

Distance grows logarithmically with graph size in the supercritical regime.

Finite covariance between in-degree and out-degree influences distance behavior.

Coupling method is applicable for a larger number of exploration steps than previous approaches.

Abstract

We analyze the distribution of the distance between two nodes, sampled uniformly at random, in digraphs generated via the directed configuration model, in the supercritical regime. Under the assumption that the covariance between the in-degree and out-degree is finite, we show that the distance grows logarithmically in the size of the graph. In contrast with the undirected case, this can happen even when the variance of the degrees is infinite. The main tool in the analysis is a new coupling between a breadth-first graph exploration process and a suitable branching process based on the Kantorovich-Rubinstein metric. This coupling holds uniformly for a much larger number of steps in the exploration process than existing ones, and is therefore of independent interest.