Traffic-driven epidemic spreading on scale-free networks with tunable degree distribution

TL;DR

This paper investigates how the structure of scale-free networks influences traffic-driven epidemic spreading, revealing that the epidemic threshold varies with degree distribution and highlighting the role of node betweenness.

Contribution

It introduces a model analyzing epidemic spreading on scale-free networks with tunable degree distribution, emphasizing the impact of network heterogeneity and node betweenness.

Findings

Epidemic threshold is minimized at degree exponent ~2.2.

Nodes with higher algorithmic betweenness are more susceptible to infection.

Network heterogeneity significantly affects epidemic spreading dynamics.

Abstract

We study the traffic-driven epidemic spreading on scale-free networks with tunable degree distribution. The heterogeneity of networks is controlled by the exponent $\gamma$ of power-law degree distribution. It is found that the epidemic threshold is minimized at about $\gamma=2.2$. Moreover, we find that nodes with larger algorithmic betweenness are more likely to be infected. We expect our work to provide new insights into the effect of network structures on traffic-driven epidemic spreading.