IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0200091.html
   My bibliography  Save this article

Identifying influential nodes based on network representation learning in complex networks

Author

Listed:
  • Hao Wei
  • Zhisong Pan
  • Guyu Hu
  • Liangliang Zhang
  • Haimin Yang
  • Xin Li
  • Xingyu Zhou

Abstract

Identifying influential nodes is an important topic in many diverse applications, such as accelerating information propagation, controlling rumors and diseases. Many methods have been put forward to identify influential nodes in complex networks, ranging from node centrality to diffusion-based processes. However, most of the previous studies do not take into account overlapping communities in networks. In this paper, we propose an effective method based on network representation learning. The method considers not only the overlapping communities in networks, but also the network structure. Experiments on real-world networks show that the proposed method outperforms many benchmark algorithms and can be used in large-scale networks.

Suggested Citation

  • Hao Wei & Zhisong Pan & Guyu Hu & Liangliang Zhang & Haimin Yang & Xin Li & Xingyu Zhou, 2018. "Identifying influential nodes based on network representation learning in complex networks," PLOS ONE, Public Library of Science, vol. 13(7), pages 1-13, July.
    • Handle: RePEc:plo:pone00:0200091
    DOI: 10.1371/journal.pone.0200091
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0200091
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0200091&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0200091?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
    ---><---

    More about this item

    Statistics

    Access and download statistics

    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:plo:pone00:0200091. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.