Identifying influential spreaders in complex network based on the node’s weight and spreading probability

Tao Ren (), Yanjie Xu and Pengyu Wang ()
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Tao Ren: Software College, Northeastern University, No. 195, Chuangxin Road, Hunnan District, Shenyang 110169, P. R. China
Yanjie Xu: Software College, Northeastern University, No. 195, Chuangxin Road, Hunnan District, Shenyang 110169, P. R. China
Pengyu Wang: Software College, Northeastern University, No. 195, Chuangxin Road, Hunnan District, Shenyang 110169, P. R. China

International Journal of Modern Physics C (IJMPC), 2024, vol. 35, issue 11, 1-17

Abstract: Identifying influential spreaders is a crucial aspect of network science with various applications, including rumor control, viral marketing and epidemic spread limitation. Despite the availability of various methods for identifying these spreaders in complex networks, there remains a fundamental question regarding their accurate and discriminative identification. To address the issues and account for each node’s propagation ability, we propose an algorithm to identify influential spreaders based on the node’s weight and spreading probability (NWSP) for identifying influential spreaders. The effectiveness of the proposed method is evaluated using the Susceptible–Infected–Recovered (SIR) model, Kendall’s Tau (τ) and monotonicity. The proposed method is compared with several well-known metrics, including degree centrality, K-shell decomposition, betweenness centrality, closeness centrality, eigenvector centrality and the centrality method based on node spreading probability (SPC), in ten real networks. Experimental results demonstrate the superiority ability of the proposed algorithm to accurately and discriminatively identify influential spreaders.

Keywords: Complex networks; influential spreader; weight; spreading probability (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1142/S0129183124501420

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