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Influential node identification by aggregating local structure information

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  • Wang, Feifei
  • Sun, Zejun
  • Gan, Quan
  • Fan, Aiwan
  • Shi, Hesheng
  • Hu, Haifeng

Abstract

In complex networks, the identification of influential nodes is very important to study the transmission and control of viruses, the location of key points of network attacks, the spread of public opinion, and the marketing promotion of markets. Therefore, based on analysis of the existing algorithms for the identification of influential nodes in complex networks, this paper proposes a new method to identify influential nodes by aggregating local structure information (ALSI). This method considers two factors: the influence of the node itself and the influence contributed by the neighbor node. The degree and K-shell value of the node are introduced when calculating the influence of the node itself, and the degree and K-shell value of the neighbor node are introduced in the calculation of the influence contributed by the neighbor node. Different calculation methods are adopted according to the comparison result of the K-shell value with the node. The greater the K-shell value and the node degree are, the more important the node is. To evaluate the performance of the algorithm, the susceptible–infected–recovered (SIR) model is used to analyze and compare the running results of 9 algorithms on 8 different networks. The experimental results show that the proposed algorithm can effectively detect the influence of nodes and outperforms many state-of-the-art algorithms.

Suggested Citation

  • Wang, Feifei & Sun, Zejun & Gan, Quan & Fan, Aiwan & Shi, Hesheng & Hu, Haifeng, 2022. "Influential node identification by aggregating local structure information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 593(C).
    • Handle: RePEc:eee:phsmap:v:593:y:2022:i:c:s037843712200022x
    DOI: 10.1016/j.physa.2022.126885
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    References listed on IDEAS

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    1. Gert Sabidussi, 1966. "The centrality index of a graph," Psychometrika, Springer;The Psychometric Society, vol. 31(4), pages 581-603, December.
    2. Fan Yang & Ruisheng Zhang & Zhao Yang & Rongjing Hu & Mengtian Li & Yongna Yuan & Keqin Li, 2017. "Identifying the most influential spreaders in complex networks by an Extended Local K-Shell Sum," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 28(01), pages 1-17, January.
    3. Zhao, Jie & Wang, Yunchuan & Deng, Yong, 2020. "Identifying influential nodes in complex networks from global perspective," Chaos, Solitons & Fractals, Elsevier, vol. 133(C).
    4. Cai Gao & Xin Lan & Xiaoge Zhang & Yong Deng, 2013. "A Bio-Inspired Methodology of Identifying Influential Nodes in Complex Networks," PLOS ONE, Public Library of Science, vol. 8(6), pages 1-11, June.
    5. Yang, Jianmei & Yao, Canzhong & Ma, Weicheng & Chen, Guanrong, 2010. "A study of the spreading scheme for viral marketing based on a complex network model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(4), pages 859-870.
    6. Yu, Hui & Cao, Xi & Liu, Zun & Li, Yongjun, 2017. "Identifying key nodes based on improved structural holes in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 318-327.
    7. Wen, Tao & Jiang, Wen, 2019. "Identifying influential nodes based on fuzzy local dimension in complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 119(C), pages 332-342.
    8. Xu, Runjie & Mi, Chuanmin & Mierzwiak, Rafał & Meng, Runyu, 2020. "Complex network construction of Internet finance risk," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
    9. Leo Katz, 1953. "A new status index derived from sociometric analysis," Psychometrika, Springer;The Psychometric Society, vol. 18(1), pages 39-43, March.
    Full references (including those not matched with items on IDEAS)

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