Opinion formation in multiplex networks with general initial distributions

TL;DR

This paper analyzes opinion dynamics in multiplex networks with diverse initial opinions and multiple confidence thresholds, revealing critical points for consensus and how multiplexity influences social consensus formation.

Contribution

It provides an analytical framework for understanding opinion evolution in multiplex networks with heterogeneous initial distributions and multiple confidence bounds.

Findings

Identified critical thresholds for phase transitions in opinion consensus.

Showed multiplexity can hinder consensus under certain initial conditions.

Validated theoretical bounds through numerical simulations on various network topologies.

Abstract

We study opinion dynamics over multiplex networks where agents interact with bounded confidence. Namely, two neighbouring individuals exchange opinions and compromise if their opinions do not differ by more than a given threshold. In literature, agents are generally assumed to have a homogeneous confidence bound. Here, we study analytically and numerically opinion evolution over structured networks characterised by multiple layers with respective confidence thresholds and general initial opinion distributions. Through rigorous probability analysis, we show analytically the critical thresholds at which a phase transition takes place in the long-term consensus behaviour, over multiplex networks with some regularity conditions. Our results reveal the quantitative relation between the critical threshold and initial distribution. Further, our numerical simulations illustrate the consensus behaviour of the agents in network topologies including lattices and, small-world and scale-free networks, as well as for structure-dependent convergence parameters accommodating node heterogeneity. We find that the critical thresholds for consensus tend to agree with the predicted upper bounds in Theorems 4 and 5 in this paper. Finally, our results indicate that multiplexity hinders consensus formation when the initial opinion configuration is within a bounded range and, provide insight into information diffusion and social dynamics in multiplex systems modeled by networks.