The birth of the strong components

TL;DR

This paper analyzes the phase transition in the structure of random directed graphs around the critical point p=1/n, providing exact probabilities for strong connectivity properties and extending the analysis to various digraph models using advanced combinatorial and analytic techniques.

Contribution

It establishes the exact limiting probability of certain strong connectivity structures in random digraphs at the phase transition, using a symbolic and saddle point approach, extending previous asymptotic results.

Findings

Exact limiting probability of strong components as a function of μ

Probability that a random digraph is acyclic or has a complex component with given excess

Extension of results to multiple complex components with specified excesses

Abstract

Random directed graphs $D(n,p)$ undergo a phase transition around the point $p = 1/n$, and the width of the transition window has been known since the works of Luczak and Seierstad. They have established that as $n \to \infty$ when $p = (1 + \mu n^{-1/3})/n$, the asymptotic probability that the strongly connected components of a random directed graph are only cycles and single vertices decreases from 1 to 0 as $\mu$ goes from $-\infty$ to $\infty$. By using techniques from analytic combinatorics, we establish the exact limiting value of this probability as a function of $\mu$ and provide more properties of the structure of a random digraph around, below and above its transition point. We obtain the limiting probability that a random digraph is acyclic and the probability that it has one strongly connected complex component with a given difference between the number of edges and vertices (called excess). Our result can be extended to the case of several complex components with given excesses as well in the whole range of sparse digraphs. Our study is based on a general symbolic method which can deal with a great variety of possible digraph families, and a version of the saddle point method which can be systematically applied to the complex contour integrals appearing from the symbolic method. While the technically easiest model is the model of random multidigraphs, in which multiple edges are allowed, and where edge multiplicities are sampled independently according to a Poisson distribution with a fixed parameter $p$, we also show how to systematically approach the family of simple digraphs, where multiple edges are forbidden, and where 2-cycles are either allowed or not. Our theoretical predictions are supported by numerical simulations, and we provide tables of numerical values for the integrals of Airy functions that appear in this study.