Coexistence of positive and negative information in information-epidemic dynamics on multiplex networks

TL;DR

This study explores how positive and negative information coexist and spread in multiplex networks, revealing distinct patterns and thresholds through analytical and simulation methods, enhancing understanding of complex information-epidemic interactions.

Contribution

It provides the first analytical solution for coexistence thresholds of positive and negative information in multiplex networks, supported by robust theoretical and simulation results.

Findings

Identified coexistence conditions for positive and negative information.

Discovered monotonic and non-monotonic spreading patterns.

Validated theoretical predictions with Monte Carlo simulations.

Abstract

This paper investigates the coexistence of positive and negative information in the context of information-epidemic dynamics on multiplex networks. In accordance with the tenets of mean field theory, we present not only the analytic solution of the prevalence threshold, but also the coexistence conditions of two distinct forms of information (i.e., the two phase transition points at which a single form of information becomes extinct). In regions where multiple forms of information coexist, two completely distinct patterns emerge: monotonic and non-monotonic. The physical mechanisms that give rise to these different patterns have also been elucidated. The theoretical results are robust with regard to the network structure and show a high degree of agreement with the findings of the Monte Carlo simulation.