The voter model on random regular graphs with random rewiring

TL;DR

This paper studies the voter model on random regular graphs with a dynamic rewiring process, showing that the opinion distribution converges to a Fisher-Wright diffusion with an explicit diffusion constant depending on graph degree and rewiring rate.

Contribution

It introduces a model combining opinion dynamics and random edge rewiring, deriving the limiting diffusion process and explicitly characterizing its diffusion constant.

Findings

The fraction of opinions converges to a Fisher-Wright diffusion as the graph size grows.

The diffusion constant is explicitly expressed via a continued-fraction expansion.

The analysis highlights the role of discordant edges in the opinion dynamics.

Abstract

We consider the voter model with binary opinions on a random regular graph with $n$ vertices of degree $d \geq 3$, subject to a rewiring dynamics in which pairs of edges are rewired, i.e., broken into four half-edges and subsequently reconnected at random. A parameter $\nu \in (0,\infty)$ regulates the frequency at which the rewirings take place, in such a way that any given edge is rewired exponentially at a rate $\nu$ in the limit as $n\to\infty$. We show that, under the joint law of the random rewiring dynamics and the random opinion dynamics, the fraction of vertices with either one of the two opinions converges on time scale $n$ to the Fisher-Wright diffusion with an explicit diffusion constant $\vartheta_{d,\nu}$ in the limit as $n\to\infty$. In particular, we identify $\vartheta_{d,\nu}$ in terms of a continued-fraction expansion and analyse its dependence on $d$ and $\nu$. A key role in our analysis is played by the set of discordant edges, which constitutes the boundary between the sets of vertices carrying the two opinions.