Random tree-weighted graphs

TL;DR

This paper proves that random spanning trees in degree-constrained graphs converge to the Brownian continuum random tree, using a new coalescent process and Poisson approximations for graph features.

Contribution

It introduces a novel coalescent process to analyze random trees in degree-constrained graphs and establishes their convergence to the Brownian CRT.

Findings

Spanning trees in degree-constrained graphs converge to the Brownian CRT.

A new coalescent process effectively constructs random trees with fixed degree sequences.

Poisson approximation for loops and multiple edges in superimposed random graphs.

Abstract

For each $n \ge 1$, let $\mathrm{d}^n=(d^{n}(i),1 \le i \le n)$ be a sequence of positive integers with even sum $\sum_{i=1}^n d^n(i) \ge 2n$. Let $(G_n,T_n,\Gamma_n)$ be uniformly distributed over the set of simple graphs $G_n$ with degree sequence $\mathrm{d}^n$, endowed with a spanning tree $T_n$ and rooted along an oriented edge $\Gamma_n$ of $G_n$ which is not an edge of $T_n$. Under a finite variance assumption on degrees in $G_n$, we show that, after rescaling, $T_n$ converges in distribution to the Brownian continuum random tree as $n \to \infty$. Our main tool is a new version of Pitman's additive coalescent (https://doi.org/10.1006/jcta.1998.2919), which can be used to build both random trees with a fixed degree sequence, and random tree-weighted graphs with a fixed degree sequence. As an input to the proof, we also derive a Poisson approximation theorem for the number of loops and multiple edges in the superposition of a fixed graph and a random graph with a given degree sequence sampled according to the configuration model; we find this to be of independent interest.