IDEAS home Printed from
MyIDEAS: Log in (now much improved!) to save this paper

Detection and Interpretation of Communities in Complex Networks: Methods and Practical Application

Listed author(s):
  • Vincent Labatut


    (Bit Lab - Galatasaray University)

  • Jean-Michel Balasque


    (Galatasaray University - Business & Marketing Department - Galatasaray University)

Registered author(s):

    Community detection is an important part of network analysis and has become a very popular field of research. This activity resulted in a profusion of community detection algorithms, all different in some not always clearly defined sense. This makes it very difficult to select an appropriate tool when facing the concrete task of having to identify and interpret groups of nodes, relatively to a system of interest. In this article, we tackle this problem in a very practical way, from the user's point of view. We first review community detection algorithms and characterize them in terms of the nature of the communities they detect. We then focus on the methodological tools one can use to analyze the obtained community structure, both in terms of topological features and nodal attributes. To be as concrete as possible, we use a real-world social network to illustrate the application of the presented tools, and give examples of interpretation of their results from a Business Science perspective.

    If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

    File URL:
    Download Restriction: no

    Paper provided by HAL in its series Post-Print with number hal-00633653.

    in new window

    Date of creation: 2012
    Publication status: Published in Computational Social Networks: Tools, Perspectives and Applications, Springer, pp.81-113, 2012, <10.1007/978-1-4471-4048-1_4>
    Handle: RePEc:hal:journl:hal-00633653
    DOI: 10.1007/978-1-4471-4048-1_4
    Note: View the original document on HAL open archive server:
    Contact details of provider: Web page:

    References listed on IDEAS
    Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

    in new window

    1. repec:dau:papers:123456789/654 is not listed on IDEAS
    2. Mark S. Handcock & Adrian E. Raftery & Jeremy M. Tantrum, 2007. "Model-based clustering for social networks," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(2), pages 301-354.
    3. Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 193-218, December.
    4. R. Luce, 1950. "Connectivity and generalized cliques in sociometric group structure," Psychometrika, Springer;The Psychometric Society, vol. 15(2), pages 169-190, June.
    5. G. Agarwal & D. Kempe, 2008. "Modularity-maximizing graph communities via mathematical programming," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 66(3), pages 409-418, December.
    Full references (including those not matched with items on IDEAS)

    This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

    When requesting a correction, please mention this item's handle: RePEc:hal:journl:hal-00633653. 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: (CCSD)

    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 references are entirely missing, you can add them using this form.

    If the full references list an item that is present in RePEc, but the system did not link 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 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.

    This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.