Discursive Voter Models on the Supercritical Scale-Free Network

TL;DR

This paper analyzes the consensus formation time in discursive voter models on supercritical scale-free networks, revealing phase transitions and confirming mean-field predictions in high-density regimes.

Contribution

It introduces a discursive voter model with a temperature parameter on scale-free networks and characterizes its consensus dynamics, including phase transitions, in the supercritical regime.

Findings

Consensus time matches mean-field predictions in high-density regimes.

Identifies two phases of consensus speed depending on the temperature parameter.

Addresses the ultrasmall world regime with degree exponent τ in (2,3].

Abstract

The voter model is a classical interacting particle system, modelling how global consensus is formed by local imitation. We analyse the time to consensus for a particular family of voter models when the underlying structure is a scale-free inhomogeneous random graph, in the high edge density regime where this graph features a giant component. In this regime, we verify that the polynomial orders of consensus agree with those of their mean-field approximation in [Moinet et al., 2018]. This "discursive" family of models has a symmetrised interaction to better model discussions, and is indexed by a temperature parameter which, for certain parameters of the power law tail of the network's degree distribution, is seen to produce two distinct phases of consensus speed. Our proofs rely on the well-known duality to coalescing random walks and a control on the mixing time of these walks, using the known fast mixing of the Erd\H{o}s-R\'enyi giant subgraph. Unlike in the subcritical case [Fernley and Ortgiese, 2022] which requires tail exponent of the limiting degree distribution $\tau=1+1/\gamma >3$ as well as low edge density, in the giant component case we also address the "ultrasmall world" power law exponents $\tau \in (2,3]$.