Fractal scale-free networks resistant to disease spread

TL;DR

This paper introduces a new fractal scale-free network model that exhibits resistance to disease spread, challenging previous assumptions about epidemic vulnerability in such networks, and demonstrates the importance of topological properties like fractality.

Contribution

The paper presents a novel fractal scale-free network model with unique properties and shows that disease spreading can be inhibited, highlighting the role of fractality and topology in epidemic dynamics.

Findings

Existence of nonzero epidemic thresholds in fractal networks

Fractality and large-world behavior inhibit disease spread

Power-law degree distribution alone does not determine epidemic dynamics

Abstract

In contrast to the conventional wisdom that scale-free networks are prone to epidemic propagation, in the paper we present that disease spreading is inhibited in fractal scale-free networks. We first propose a novel network model and show that it simultaneously has the following rich topological properties: scale-free degree distribution, tunable clustering coefficient, "large-world" behavior, and fractal scaling. Existing network models do not display these characteristics. Then, we investigate the susceptible-infected-removed (SIR) model of the propagation of diseases in our fractal scale-free networks by mapping it to bond percolation process. We find an existence of nonzero tunable epidemic thresholds by making use of the renormalization group technique, which implies that power-law degree distribution does not suffice to characterize the epidemic dynamics on top of scale-free networks. We argue that the epidemic dynamics are determined by the topological properties, especially the fractality and its accompanying "large-world" behavior.