1 School of Information Engineering, Nanchang Hangkong University, Nanchang 330063, China; 2 School of Automation, Nanjing University of Technology, Nanjing 210094, China
Abstract How to identify influential nodes in complex networks is an essential issue in the study of network characteristics. A number of methods have been proposed to address this problem, but most of them focus on only one aspect. Based on the gravity model, a novel method is proposed for identifying influential nodes in terms of the local topology and the global location. This method comprehensively examines the structural hole characteristics and K-shell centrality of nodes, replaces the shortest distance with a probabilistically motivated effective distance, and fully considers the influence of nodes and their neighbors from the aspect of gravity. On eight real-world networks from different fields, the monotonicity index, susceptible-infected-recovered (SIR) model, and Kendall's tau coefficient are used as evaluation criteria to evaluate the performance of the proposed method compared with several existing methods. The experimental results show that the proposed method is more efficient and accurate in identifying the influence of nodes and can significantly discriminate the influence of different nodes.
Yuan Jiang(蒋沅), Song-Qing Yang(杨松青), Yu-Wei Yan(严玉为),Tian-Chi Tong(童天驰), and Ji-Yang Dai(代冀阳) A novel method for identifying influential nodes in complex networks based on gravity model 2022 Chin. Phys. B 31 058903
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