Limit Laws for Critical Dispersion on Complete Graphs

TL;DR

The paper investigates a critical dispersion regime for a particle-dispersion process on the complete graph with $M=n/2+\alpha\sqrt n+o(\sqrt n)$. By establishing a diffusion-approximation bridge to a standard logistic Feller diffusion, it shows that the dispersion time, when scaled by $n^{1/2}$, converges in distribution to the absorption time $T_\alpha$ of a logistic diffusion whose drift depends on $\alpha$, and it provides an explicit biochemical-delivery formula for $\mathbb{E}[T_α]$, including the notable value $\mathbb{E}[T_0]=\pi^{3/2}/\sqrt{7}$. The work further characterizes the total number of jumps, proving a sharp $\frac{2}{7}n\ln n$ baseline with $O(n)$ fluctuations and a limiting distribution $A_\alpha$ that arises from the integrated logistic-diffusion process. Collectively, these results illuminate the smooth transition through the critical window and connect discrete dispersion dynamics to continuous diffusion theory with tractable absorbing-time statistics, enabling precise asymptotics and explicit tail behavior in both directions of the window.

Abstract

We consider a synchronous process of particles moving on the vertices of a graph $G$, introduced by Cooper, McDowell, Radzik, Rivera and Shiraga (2018). Initially, $M$ particles are placed on a vertex of $G$. In subsequent time steps, all particles that are located on a vertex inhabited by at least two particles jump independently to a neighbour chosen uniformly at random. The process ends at the first step when no vertex is inhabited by more than one particle; we call this (random) time step the dispersion time. In this work we study the case where $G$ is the complete graph on $n$ vertices and the number of particles is $M=n/2+αn^{1/2} + o(n^{1/2})$, $α\in \mathbb{R}$. This choice of $M$ corresponds to the critical window of the process, with respect to the dispersion time. We show that the dispersion time, if rescaled by $n^{-1/2}$, converges in $p$-th mean, as $n\rightarrow \infty$ and for any $p \in \mathbb{R}$, to a continuous and almost surely positive random variable $T_α$. We find that $T_α$ is the absorption time of a standard logistic branching process, thoroughly investigated by Lambert (2005), and we determine its expectation. In particular, in the middle of the critical window we show that $\mathbb{E}[T_0] = π^{3/2}/\sqrt{7}$, and furthermore we formulate explicit asymptotics when $|α|$ gets large that quantify the transition into and out of the critical window. We also study the (random) total number of jumps that are performed by the particles until the dispersion time is reached. In particular, we prove that it centers around $\frac{2}{7}n\ln n$ and that it has variations linear in $n$, whose distribution we can describe explicitly.

Figures (2)

• Figure 1: The typical order of $T_{n,M}$ when $M = (1+\varepsilon)n/2$ and $|\varepsilon| = o(1)$. Note that $|\varepsilon^{-1}|\ln(\varepsilon^2 n)$ and $\varepsilon^{-1} \exp(\varepsilon^2 n)$ are in $\Theta(n^{1/2})$ when $|\varepsilon| = \Theta(n^{-1/2})$, and so the transition into and out of the critical window is smooth.
• Figure 2: Three sample runs of the dispersion process with $n=10^7$ and $M = n/2$, where we depict the number of unhappy particles $U_t$, divided by 1000, at each step $t$. The trajectory is revealed only after $t'=500$, where $U_{t'} \approx 10^4 \approx 3n^{1/2}$ in all cases. The dotted line represents the iterated mean of $U_t$, see also Lemma \ref{['lem:veryEarly']}. For the asymptotics of $\mathbb{E}[T_{n,M}]$ see \ref{['eq:ET0']}.

Theorems & Definitions (57)

• Theorem 1.1
• Lemma 1.2
• Theorem 1.3
• Theorem 1.4
• Theorem 2.1: Diffusion Approximation
• Lemma 2.2
• Corollary 2.3
• Lemma 2.4
• proof
• Lemma 2.5