Jin Liu, Wenbin Yu, ChengJun Zhang, JiaRui Gu, Louyang Yu, Guancheng Zhong

Physica A: Statistical Mechanics and its Applications

Identifying influential spreaders in complex networks remains a significant research topic. Previous studies have primarily focused on estimating the source of spread. Our research focuses on identifying whether an infected node has sustained infection capabilities during the spreading process. We define a node with a continuous infection capability as an active node with high node activity. We propose an algorithm based on node centrality to calculate the node activity. Unlike the established paradigms, we posit that node centrality is negatively correlated with node activity. Nodes with lower centrality exhibited higher activity and infectiousness. In contrast, nodes with higher centrality may have recovered from the infection and resulted in lower activity and a diminished capacity to propagate the virus. Experiments on artificial and empirical networks demonstrate that the proposed method can effectively identify nodes with sustained infection capability. The proposed method enhances our understanding of the spreading dynamics and provides a valuable tool for managing and controlling the spread of information or diseases in complex networks.

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