Path Counting on Tree-like Graphs with a Single Entropic Trap: Critical Behavior and Finite Size Effects

TL;DR

This paper investigates the localization transition of path counting on tree-like graphs with a single entropic trap, revealing critical behavior, finite size effects, and the dynamics of path endpoint distributions.

Contribution

It provides an exact analysis of the localization transition on regular trees and random graphs, including distribution shapes, relaxation times, and finite size effects.

Findings

Distribution of path endpoints is a step function with a velocity of (p-2)/p.

Finite RRGs exhibit critical slowdown with relaxation time on the order of √N.

Exact equilibrium distribution and relaxation modes are calculated.

Abstract

It is known that maximal entropy random walks and partition functions that count long paths on graphs tend to become localized near nodes with a high degree. Here, we revisit the simplest toy model of such a localization: a regular tree of degree $p$ with one special node ("root") that has a degree different from all the others. We present an in-depth study of the path-counting problem precisely at the localization transition. We study paths that start from the root in both infinite trees and finite, locally tree-like regular random graphs (RRGs). For the infinite tree, we prove that the probability distribution function of the endpoints of the path is a step function. The position of the step moves away from the root at a constant velocity $v=(p-2)/p$. We find the width and asymptotic shape of the distribution in the vicinity of the shock. For a finite RRG, we show that a critical slowdown takes place, and the trajectory length needed to reach the equilibrium distribution is on the order of $\sqrt{N}$ instead of $\log_{p-1}N$ away from the transition. We calculate the exact values of the equilibrium distribution and relaxation length, as well as the shapes of slowly relaxing modes.