IDEAS home Printed from
   My bibliography  Save this article

Detection of community structure in networks based on community coefficients


  • Lu, Hu
  • Wei, Hui


Determining community structure in networks is fundamental to the analysis of the structural and functional properties of those networks, including social networks, computer networks, and biological networks. Modularity function Q, which was proposed by Newman and Girvan, was once the most widely used criterion for evaluating the partition of a network into communities. However, modularity Q is subject to a serious resolution limit. In this paper, we propose a new function for evaluating the partition of a network into communities. This is called community coefficient C. Using community coefficient C, we can automatically identify the ideal number of communities in the network, without any prior knowledge. We demonstrate that community coefficient C is superior to the modularity Q and does not have a resolution limit. We also compared the two widely used community structure partitioning methods, the hierarchical partitioning algorithm and the normalized cuts (Ncut) spectral partitioning algorithm. We tested these methods on computer-generated networks and real-world networks whose community structures were already known. The Ncut algorithm and community coefficient C were found to produce better results than hierarchical algorithms. Unlike several other community detection methods, the proposed method effectively partitioned the networks into different community structures and indicated the correct number of communities.

Suggested Citation

  • Lu, Hu & Wei, Hui, 2012. "Detection of community structure in networks based on community coefficients," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(23), pages 6156-6164.
  • Handle: RePEc:eee:phsmap:v:391:y:2012:i:23:p:6156-6164
    DOI: 10.1016/j.physa.2012.06.062

    Download full text from publisher

    File URL:
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

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

    References listed on IDEAS

    1. Zhang, Junhua & Zhang, Shihua & Zhang, Xiang-Sun, 2008. "Detecting community structure in complex networks based on a measure of information discrepancy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(7), pages 1675-1682.
    2. Zhang, Shihua & Wang, Rui-Sheng & Zhang, Xiang-Sun, 2007. "Identification of overlapping community structure in complex networks using fuzzy c-means clustering," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 374(1), pages 483-490.
    Full references (including those not matched with items on IDEAS)


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

    Cited by:

    1. Li, Wei & Huang, Ce & Wang, Miao & Chen, Xi, 2017. "Stepping community detection algorithm based on label propagation and similarity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 472(C), pages 145-155.


    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:eee:phsmap:v:391:y:2012:i:23:p:6156-6164. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dana Niculescu). General contact details of provider: .

    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.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.