An improved dynamic-sensitive centrality based on interactive influence for identifying influential nodes in aviation networks

Linfeng Zhong, Pengfei Chen, Fei Hu, Jin Huang, Qingwei Zhong, Xiangying Gao, Hao Yang and Lei Zhang
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Linfeng Zhong: School of Air Traffic Management, Civil Aviation Flight University of China, Guanghan 618300, P. R. China†Chengdu GoldTel Industry Group Co., Ltd., Chengdu 610000, P. R. China‡University of Electronic Science and Technology of China, Chengdu 610000, P. R. China
Pengfei Chen: School of Air Traffic Management, Civil Aviation Flight University of China, Guanghan 618300, P. R. China
Fei Hu: �Civil Aviation Flight University of China Suining Flight College, Suining 629000, P. R. China
Jin Huang: School of Air Traffic Management, Civil Aviation Flight University of China, Guanghan 618300, P. R. China
Qingwei Zhong: School of Air Traffic Management, Civil Aviation Flight University of China, Guanghan 618300, P. R. China
Xiangying Gao: School of Air Traffic Management, Civil Aviation Flight University of China, Guanghan 618300, P. R. China
Hao Yang: School of Air Traffic Management, Civil Aviation Flight University of China, Guanghan 618300, P. R. China
Lei Zhang: School of Air Traffic Management, Civil Aviation Flight University of China, Guanghan 618300, P. R. China

International Journal of Modern Physics C (IJMPC), 2025, vol. 36, issue 10, 1-14

Abstract: The identification of influential nodes in complex networks is a hot topic among scholars. Classical methods commonly analyze single node information or static structures but seldom emphasize dynamic properties and the interactive influence of nodes. Here, we proposed an improved Dynamic-Sensitive centrality (IDS) method by considering the interactive influence of both the self and neighbor nodes. Based on six real aviation networks and the Susceptible Infected Recovered (SIR) spreading disease model, we simulated the actual spreading process within these networks. Relevant experiments were conducted through Kendall’s correlation coefficient, the imprecision function, and the complementary cumulative distribution function. The experimental results demonstrated that the IDS can more accurately identify the influential node and effectively differentiate the node influence in the network compared with other benchmark methods. Especially in the EU air-2 network, the IDS results in Kendall’s correlation coefficient are improved by 105% compared to the DS centrality.

Keywords: Aviation network; influential node; dynamic; interactive influence; SIR (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1142/S0129183124420075

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