IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0103733.html
   My bibliography  Save this article

Identifying Node Role in Social Network Based on Multiple Indicators

Author

Listed:
  • Shaobin Huang
  • Tianyang Lv
  • Xizhe Zhang
  • Yange Yang
  • Weimin Zheng
  • Chao Wen

Abstract

It is a classic topic of social network analysis to evaluate the importance of nodes and identify the node that takes on the role of core or bridge in a network. Because a single indicator is not sufficient to analyze multiple characteristics of a node, it is a natural solution to apply multiple indicators that should be selected carefully. An intuitive idea is to select some indicators with weak correlations to efficiently assess different characteristics of a node. However, this paper shows that it is much better to select the indicators with strong correlations. Because indicator correlation is based on the statistical analysis of a large number of nodes, the particularity of an important node will be outlined if its indicator relationship doesn't comply with the statistical correlation. Therefore, the paper selects the multiple indicators including degree, ego-betweenness centrality and eigenvector centrality to evaluate the importance and the role of a node. The importance of a node is equal to the normalized sum of its three indicators. A candidate for core or bridge is selected from the great degree nodes or the nodes with great ego-betweenness centrality respectively. Then, the role of a candidate is determined according to the difference between its indicators' relationship with the statistical correlation of the overall network. Based on 18 real networks and 3 kinds of model networks, the experimental results show that the proposed methods perform quite well in evaluating the importance of nodes and in identifying the node role.

Suggested Citation

  • Shaobin Huang & Tianyang Lv & Xizhe Zhang & Yange Yang & Weimin Zheng & Chao Wen, 2014. "Identifying Node Role in Social Network Based on Multiple Indicators," PLOS ONE, Public Library of Science, vol. 9(8), pages 1-16, August.
  • Handle: RePEc:plo:pone00:0103733
    DOI: 10.1371/journal.pone.0103733
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0103733
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0103733&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0103733?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
    ---><---

    Citations

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


    Cited by:

    1. Shakibian, Hadi & Charkari, Nasrollah Moghadam, 2018. "Statistical similarity measures for link prediction in heterogeneous complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 501(C), pages 248-263.
    2. Jiang, Syuan-Yi, 2022. "Transition and innovation ecosystem – investigating technologies, focal actors, and institution in eHealth innovations," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    3. Cirunay, Michelle T. & Batac, Rene C., 2023. "Evolution of the periphery of a self-organized road network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 617(C).
    4. Miyoung Chong & Hae Jung Maria Kim, 2020. "Social roles and structural signatures of top influentials in the #prayforparis Twitter network," Quality & Quantity: International Journal of Methodology, Springer, vol. 54(1), pages 315-333, February.

    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:pone00:0103733. 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.