Traffic-driven epidemic spreading in correlated networks

TL;DR

This paper investigates how traffic-driven epidemic spreading behaves in correlated networks, revealing a non-monotonic epidemic threshold influenced by network assortativity and providing a theoretical framework for understanding and controlling such spreading.

Contribution

It introduces a degree-based mean-field theory to analyze traffic-driven epidemic thresholds in correlated networks, highlighting the non-monotonic behavior related to network assortativity.

Findings

Epidemic threshold exhibits non-monotonic behavior with respect to assortativity.

Threshold is inversely proportional to packet-generation rate and largest eigenvalue of betweenness matrix.

Theory and simulations are consistent in predicting epidemic dynamics.

Abstract

In spite of the extensive previous efforts on traffic dynamics and epidemic spreading in complex networks, the problem of traffic-driven epidemic spreading on {\em correlated} networks has not been addressed. Interestingly, we find that the epidemic threshold, a fundamental quantity underlying the spreading dynamics, exhibits a non-monotonic behavior in that it can be minimized for some critical value of the assortativity coefficient, a parameter characterizing the network correlation. To understand this phenomenon, we use the degree-based mean-field theory to calculate the traffic-driven epidemic threshold for correlated networks. The theory predicts that the threshold is inversely proportional to the packet-generation rate and the largest eigenvalue of the betweenness matrix. We obtain consistency between theory and numerics. Our results may provide insights into the important problem of controlling/harnessing real-world epidemic spreading dynamics driven by traffic flows.