Persistence of centrality in random growing trees

TL;DR

This paper studies node centrality persistence in various random growing tree models, proving that the most central node remains so after finite steps with high probability and establishing conditions for initial seed size to ensure persistence.

Contribution

It introduces a formal analysis of centrality persistence in random trees and derives bounds on initial seed size needed for guaranteed persistence.

Findings

Single node persists as centroid after finite steps with probability 1

Persistence extends to top K nodes under the same measure

Bounds on initial seed size ensure persistence with high probability

Abstract

We investigate properties of node centrality in random growing tree models. We focus on a measure of centrality that computes the maximum subtree size of the tree rooted at each node, with the most central node being the tree centroid. For random trees grown according to a preferential attachment model, a uniform attachment model, or a diffusion processes over a regular tree, we prove that a single node persists as the tree centroid after a finite number of steps, with probability 1. Furthermore, this persistence property generalizes to the top $K \ge 1$ nodes with respect to the same centrality measure. We also establish necessary and sufficient conditions for the size of an initial seed graph required to ensure persistence of a particular node with probability $1-\epsilon$, as a function of $\epsilon$: In the case of preferential and uniform attachment models, we derive bounds for the size of an initial hub constructed around the special node. In the case of a diffusion process over a regular tree, we derive bounds for the radius of an initial ball centered around the special node. Our necessary and sufficient conditions match up to constant factors for preferential attachment and diffusion tree models.