Immunization of networks with non-overlapping community structure

TL;DR

This paper introduces three deterministic immunization strategies that leverage non-overlapping community structures in networks to more effectively control epidemic spread, outperforming traditional methods.

Contribution

It proposes novel community-aware immunization strategies and demonstrates their superior effectiveness through simulations on real and synthetic networks.

Findings

Community-aware strategies outperform classical methods.

Proposed strategies are more effective in controlling epidemics.

Strategies outperform existing stochastic and deterministic approaches.

Abstract

Although community structure is ubiquitous in complex networks, few works exploit this topological property to control epidemics. In this work, devoted to networks with non-overlapping community structure (i.e, a node belongs to a single community), we propose and investigate three deterministic immunization strategies. In order to characterize the influence of a node, various pieces of information are used such as the number of communities that the node can reach in one hop, the nature of the links (intra community links, inter community links), the size of the communities, and the interconnection density between communities. Numerical simulations with the Susceptible-Infected-Removed (SIR) epidemiological model are conducted on both real-world and synthetic networks. Experimental results show that the proposed strategies are more effective than classical deterministic alternatives that are agnostic of the community structure. Additionally, they outperform stochastic and deterministic strategies designed for modular networks.