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A novel measure for influence nodes across complex networks based on node attraction

Author

Listed:
  • Bin Wang

    (School of Computer Science and Engineering, Central South University Changsha, Hunan, P. R. China)

  • Wanghao Guan

    (School of Computer Science and Engineering, Central South University Changsha, Hunan, P. R. China)

  • Yuxuan Sheng

    (School of Computer Science and Engineering, Central South University Changsha, Hunan, P. R. China)

  • Jinfang Sheng

    (School of Computer Science and Engineering, Central South University Changsha, Hunan, P. R. China)

  • Jinying Dai

    (School of Computer Science and Engineering, Central South University Changsha, Hunan, P. R. China)

  • Junkai Zhang

    (School of Computer Science and Engineering, Central South University Changsha, Hunan, P. R. China)

  • Qiong Li

    (School of Computer Science and Engineering, Central South University Changsha, Hunan, P. R. China)

  • Qiangqiang Dong

    (School of Computer Science and Engineering, Central South University Changsha, Hunan, P. R. China)

  • Long Chen

    (School of Computer Science and Engineering, Central South University Changsha, Hunan, P. R. China)

Abstract

The real-world network is heterogeneous, and it is an important and challenging task to effectively identify the influential nodes in complex networks. Identification of influential nodes is widely used in social, biological, transportation, information and other networks with complex structures to help us solve a variety of complex problems. In recent years, the identification of influence nodes has received a lot of attention, and scholars have proposed various methods based on different practical problems. This paper proposes a new method to identify influential nodes, namely Attraction based on Node and Community (ANC). By considering the attraction of nodes to nodes and nodes to community structure, this method quantifies the attraction of a node, and the attraction of a node is used to represent its influence. To illustrate the effectiveness of ANC, we did extensive experiments on six real-world networks and the results show that the ANC algorithm is superior to the representative algorithms in terms of the accuracy and has lower time complexity as well.

Suggested Citation

  • Bin Wang & Wanghao Guan & Yuxuan Sheng & Jinfang Sheng & Jinying Dai & Junkai Zhang & Qiong Li & Qiangqiang Dong & Long Chen, 2021. "A novel measure for influence nodes across complex networks based on node attraction," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 32(01), pages 1-19, January.
    • Handle: RePEc:wsi:ijmpcx:v:32:y:2021:i:01:n:s0129183121500121
    DOI: 10.1142/S0129183121500121
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    Cited by:

    1. Wang, Shuliang & Dong, Qiqi, 2023. "A multi-source power grid's resilience enhancement strategy based on subnet division and power dispatch," International Journal of Critical Infrastructure Protection, Elsevier, vol. 41(C).
    2. Yuping Jin & Yanbin Yang & Wei Liu, 2022. "Finding Global Liquefied Natural Gas Potential Trade Relations Based on Improved Link Prediction," Sustainability, MDPI, vol. 14(19), pages 1-22, September.
    3. Gao, Xin & Ye, Yunxia & Su, Wenxin & Chen, Linyan, 2023. "Assessing the comprehensive importance of power grid nodes based on DEA," International Journal of Critical Infrastructure Protection, Elsevier, vol. 42(C).

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