A generalised mean-field approximation for the Deffuant opinion dynamics model on networks

TL;DR

This paper introduces a generalized mean-field approximation for the Deffuant opinion dynamics model on networks, effectively capturing the influence of network topology on opinion evolution.

Contribution

A novel generalized mean-field approximation that incorporates network structure effects into Deffuant opinion dynamics modeling.

Findings

The approximation accurately predicts opinion dynamics on synthetic networks.

The approximation aligns well with Monte Carlo simulations on real-world networks.

Network topology significantly influences opinion consensus and polarization.

Abstract

When the interactions of agents on a network are assumed to follow the Deffuant opinion dynamics model, the outcomes are known to depend on the structure of the underlying network. This behavior cannot be captured by existing mean-field approximations for the Deffuant model. In this paper, a generalised mean-field approximation is derived that accounts for the effects of network topology on Deffuant dynamics through the degree distribution or community structure of the network. The accuracy of the approximation is examined by comparison with large-scale Monte Carlo simulations on both synthetic and real-world networks.