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Identifying influential nodes in complex networks based on expansion factor

Author

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  • Dong Liu

    (School of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, P. R. China†Engineering Technology Research Center for Computing Intelligence and Data Mining, Henan Province, Xinxiang 453007, P. R. China)

  • Yun Jing

    (School of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, P. R. China)

  • Baofang Chang

    (School of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, P. R. China)

Abstract

Identifying the top influential spreaders in a network has practical significance. In this paper, we propose a novel centrality to identify influential spreaders based on expansion factor. Nodes with high expansion factor centrality (EFC) have strong spreading capability. During the course of the work, an improved strategy is proposed to reduce the time complexity of EFC. We discuss the correlations between EFC and the other five classical indicators. Simulation results on the Susceptible-Infected-Removed (SIR) model manifest that EFC can identify influential nodes and find some critical influential nodes neglected by other indicators.

Suggested Citation

  • Dong Liu & Yun Jing & Baofang Chang, 2016. "Identifying influential nodes in complex networks based on expansion factor," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 27(09), pages 1-16, September.
    • Handle: RePEc:wsi:ijmpcx:v:27:y:2016:i:09:n:s0129183116501059
    DOI: 10.1142/S0129183116501059
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    Citations

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    Cited by:

    1. Lv, Zhiwei & Zhao, Nan & Xiong, Fei & Chen, Nan, 2019. "A novel measure of identifying influential nodes in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 488-497.
    2. Luo, Mengzhuo & Liu, Xinzhi & Zhong, Shouming & Cheng, Jun, 2018. "Synchronization of multi-stochastic-link complex networks via aperiodically intermittent control with two different switched periods," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 20-38.
    3. Jiayu Qin & Gang Mei & Lei Xiao, 2020. "Building the Traffic Flow Network with Taxi GPS Trajectories and Its Application to Identify Urban Congestion Areas for Traffic Planning," Sustainability, MDPI, vol. 13(1), pages 1-18, December.
    4. Li, Meizhu & Zhang, Qi & Deng, Yong, 2018. "Evidential identification of influential nodes in network of networks," Chaos, Solitons & Fractals, Elsevier, vol. 117(C), pages 283-296.

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