IDEAS home Printed from https://ideas.repec.org/a/wsi/ijmpcx/v28y2017i01ns0129183117500140.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://www.worldscientific.com/doi/abs/10.1142/S0129183117500140
    Download Restriction: Access to full text is restricted to subscribers

    File URL: https://libkey.io/10.1142/S0129183117500140?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wsi:ijmpcx:v:28:y:2017:i:01:n:s0129183117500140. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Tai Tone Lim (email available below). General contact details of provider: http://www.worldscinet.com/ijmpc/ijmpc.shtml .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.