The influence of the network topology on epidemic spreading

TL;DR

This paper investigates how the local network topology influences epidemic spreading by deriving bounds on node infection probabilities and demonstrating that small neighborhood information can accurately estimate overall infection levels.

Contribution

It introduces bounds based on local neighborhood sizes to connect microscopic topology effects with macroscopic epidemic behavior.

Findings

Bounds become tighter with larger neighborhood sizes.

2-hop neighborhood information accurately estimates infection density.

Local topology significantly influences epidemic spreading.

Abstract

The influence of the network's structure on the dynamics of spreading processes has been extensively studied in the last decade. Important results that partially answer this question show a weak connection between the macroscopic behavior of these processes and specific structural properties in the network, such as the largest eigenvalue of a topology related matrix. However, little is known about the direct influence of the network topology on microscopic level, such as the influence of the (neighboring) network on the probability of a particular node's infection. To answer this question, we derive both an upper and a lower bound for the probability that a particular node is infective in a susceptible-infective-susceptible model for two cases of spreading processes: reactive and contact processes. The bounds are derived by considering the $n-$hop neighborhood of the node; the bounds are tighter as one uses a larger $n-$hop neighborhood to calculate them. Consequently, using local information for different neighborhood sizes, we assess the extent to which the topology influences the spreading process, thus providing also a strong macroscopic connection between the former and the latter. Our findings are complemented by numerical results for a real-world e-mail network. A very good estimate for the infection density $\rho$ is obtained using only 2-hop neighborhoods which account for 0.4% of the entire network topology on average.