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Identifying the most influential spreaders in complex networks by an Extended Local K-Shell Sum

Author

Listed:
  • Fan Yang

    (School of Information Science and Engineering, Lanzhou University Lanzhou, Gansu 730000, P. R. China)

  • Ruisheng Zhang

    (School of Information Science and Engineering, Lanzhou University Lanzhou, Gansu 730000, P. R. China)

  • Zhao Yang

    (School of Information Science and Engineering, Lanzhou University Lanzhou, Gansu 730000, P. R. China)

  • Rongjing Hu

    (School of Information Science and Engineering, Lanzhou University Lanzhou, Gansu 730000, P. R. China)

  • Mengtian Li

    (School of Information Science and Engineering, Lanzhou University Lanzhou, Gansu 730000, P. R. China)

  • Yongna Yuan

    (School of Information Science and Engineering, Lanzhou University Lanzhou, Gansu 730000, P. R. China)

  • Keqin Li

    (Department of Computer Science, State University of New York New Paltz, NY 12561, USA)

Abstract

Identifying influential spreaders is crucial for developing strategies to control the spreading process on complex networks. Following the well-known K-Shell (KS) decomposition, several improved measures are proposed. However, these measures cannot identify the most influential spreaders accurately. In this paper, we define a Local K-Shell Sum (LKSS) by calculating the sum of the K-Shell indices of the neighbors within 2-hops of a given node. Based on the LKSS, we propose an Extended Local K-Shell Sum (ELKSS) centrality to rank spreaders. The ELKSS is defined as the sum of the LKSS of the nearest neighbors of a given node. By assuming that the spreading process on networks follows the Susceptible-Infectious-Recovered (SIR) model, we perform extensive simulations on a series of real networks to compare the performance between the ELKSS centrality and other six measures. The results show that the ELKSS centrality has a better performance than the six measures to distinguish the spreading ability of nodes and to identify the most influential spreaders accurately.

Suggested Citation

  • 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.
    • Handle: RePEc:wsi:ijmpcx:v:28:y:2017:i:01:n:s0129183117500140
    DOI: 10.1142/S0129183117500140
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    Cited by:

    1. Wang, Shuangyan & Cheng, Wuyi, 2019. "Novel method for spreading information with fewer resources in scale-free networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 524(C), pages 15-29.
    2. 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).

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