Depth of vertices with high degree in random recursive trees

TL;DR

This paper investigates the asymptotic behavior of the degrees and depths of vertices in random recursive trees, revealing Poisson and Gaussian limit distributions and extending joint normality results for random vertices.

Contribution

It introduces a novel connection between recursive trees and Kingman's coalescent to analyze vertex degrees and depths, providing new limit theorems and joint distribution results.

Findings

Degrees of high-degree vertices follow a Poisson point process limit.

Depths of random vertices are jointly normal in the limit.

Joint normality extends to vertices conditioned on large degree.

Abstract

Let $T_n$ be a random recursive tree with $n$ nodes. List vertices of $T_n$ in decreasing order of degree as $v^1,\ldots,v^n$, and write $d^i$ and $h^i$ for the degree of $v^i$ and the distance of $v^i$ from the root, respectively. We prove that, as $n \to \infty$ along suitable subsequences, \[ \bigg(d^i - \lfloor \log_2 n \rfloor, \frac{h^i - \mu\ln n}{\sqrt{\sigma^2\ln n}}\bigg) \to ((P_i,i \ge 1),(N_i,i \ge 1))\, , \] where $\mu=1-(\log_2 e)/2$, $\sigma^2=1-(\log_2 e)/4$, $(P_i,i \ge 1)$ is a Poisson point process on $\mathbb{Z}$ and $(N_i,i \ge 1)$ is a vector of independent standard Gaussians. We additionally establish joint normality for the depths of uniformly random vertices in $T_n$, which extends results for the case of a single random vertex. The joint limit holds even if the random vertices are conditioned to have large degree, provided the normalizing constants are adjusted accordingly; however, both the mean and variance of the conditinal depths remain of orden $\ln n$. Our results are based on a simple relationship between random recursive trees and Kingman's $n$-coalescent; a utility that seems to have been largely overlooked.