Preventing epidemic spreading in networks by community detection and memetic algorithm

Targeted immunization is a commonly used strategy in preventing epidemic spreading. Traditional methods immunize targeted nodes based on specific global or local network structures instead of optimization. In this paper, we propose a novel community-based immunization strategy to select targeted immunization nodes based on optimization. The proposed algorithm consists of three steps. First, community structures are discovered by community detection algorithm. Second, possible candidates are narrowed down based on the structure properties of community. Finally, a novel memetic algorithm is designed to select immunization nodes from the candidate set. In the final step, epidemic threshold is adopted as objective function and then targeted immunization is formulated as an optimization problem. To solve this optimization problem, a novel memetic algorithm is designed. Experimental results demonstrate that the proposed algorithm outperforms some state-of-the-art immunization algorithms in optimizing epidemic threshold. (C) 2020 Elsevier B.V. All rights reserved.