Cooperative spreading processes in multiplex networks.

Xiang Wei,Jun-An Lu,Xiaoqun Wu,Shihua Chen,Di Ning

doi:10.1063/1.4952964

Abstract

This study is concerned with the dynamic behaviors of epidemic spreading in multiplex networks. A model composed of two interacting complex networks is proposed to describe cooperative spreading processes, wherein the virus spreading in one layer can penetrate into the other to promote the spreading process. The global epidemic threshold of the model is smaller than the epidemic thresholds of the corresponding isolated networks. Thus, global epidemic onset arises in the interacting networks even though an epidemic onset does not arise in each isolated network. Simulations verify the analysis results and indicate that cooperative spreading processes in multiplex networks enhance the final infection fraction.

Highlights

Many spreading processes have been studied independently in a single network
This study aims to prove that the epidemic thresholds of interacting two-layer networks can be decreased for cooperative spreading processes, which implies that cooperative spreading processes promote the spread of disease
It is revealed that a network with a small epidemic threshold confirms the global epidemic threshold of the multiplex networks

Summary

INTRODUCTION

Many spreading processes have been studied independently in a single network. Saumell-Mendiola et al. analyzed an epidemic spreading process of two interacting networks They developed a heterogeneous mean-field approach and revealed that a global endemic state may arise in the coupled system even though two networks are well below. Perturbation analysis theory is used to prove that epidemic thresholds of interacting two-layer networks can be decreased in cooperative spreading processes. This theory reveals that the cooperative interaction between two spread processes can promote spread. A global epidemic threshold for interacting networks exists This theory displays that cooperative spreading processes in multiplex network enhance the final infection fraction.

NETWORK MODELING AND PRELIMINARIES

P1 I P2

NUMERICAL SIMULATIONS

CONCLUSION