IDEAS home Printed from https://ideas.repec.org/a/wsi/ijmpcx/v32y2021i01ns0129183121500121.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://www.worldscientific.com/doi/abs/10.1142/S0129183121500121
    Download Restriction: Access to full text is restricted to subscribers
    File URL: https://libkey.io/10.1142/S0129183121500121?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. 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).
    2. 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).
    3. 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.

    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:32:y:2021:i:01:n:s0129183121500121. 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.