On the separability of structural classes of communities

Bruno Abrahao, Sucheta Soundarajan, John Hopcroft, Robert Kleinberg

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    Three major factors govern the intricacies of community extraction in networks: (1) the application domain includes a wide variety of networks of fundamentally different natures, (2) the literature offers a multitude of disparate community detection algorithms, and (3) there is no consensus characterizing how to discriminate communities from non-communities. In this paper, we present a comprehensive analysis of community properties through a class separability framework. Our approach enables the assessement of the structural dissimilarity among the output of multiple community detection algorithms and between the output of algorithms and communities that arise in practice. To demostrate this concept, we furnish our method with a large set of structural properties and multiple community detection algorithms. Applied to a diverse collection of large scale network datasets, the analysis reveals that (1) the different detection algorithms extract fundamentally different structures; (2) the structure of communities that arise in practice is closest to that of communities that random-walk-based algorithms extract, although still siginificantly different from that of the output of all the algorithms; and (3) a small subset of the properties are nearly as discriminative as the full set, while making explicit the ways in which the algorithms produce biases. Our framework enables an informed choice of the most suitable community detection method for a given purpose and network and allows for a comparison of existing community detection algorithms while guiding the design of new ones.

    Original languageEnglish (US)
    Title of host publicationKDD'12 - 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
    Pages624-632
    Number of pages9
    DOIs
    StatePublished - Sep 14 2012
    Event18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2012 - Beijing, China
    Duration: Aug 12 2012Aug 16 2012

    Publication series

    NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

    Other

    Other18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2012
    CountryChina
    CityBeijing
    Period8/12/128/16/12

    Fingerprint

    Set theory
    Structural properties

    Keywords

    • class separability
    • community structure
    • detection algorithms
    • networks

    ASJC Scopus subject areas

    • Software
    • Information Systems

    Cite this

    Abrahao, B., Soundarajan, S., Hopcroft, J., & Kleinberg, R. (2012). On the separability of structural classes of communities. In KDD'12 - 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 624-632). (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). https://doi.org/10.1145/2339530.2339631

    On the separability of structural classes of communities. / Abrahao, Bruno; Soundarajan, Sucheta; Hopcroft, John; Kleinberg, Robert.

    KDD'12 - 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2012. p. 624-632 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abrahao, B, Soundarajan, S, Hopcroft, J & Kleinberg, R 2012, On the separability of structural classes of communities. in KDD'12 - 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 624-632, 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2012, Beijing, China, 8/12/12. https://doi.org/10.1145/2339530.2339631
    Abrahao B, Soundarajan S, Hopcroft J, Kleinberg R. On the separability of structural classes of communities. In KDD'12 - 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2012. p. 624-632. (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). https://doi.org/10.1145/2339530.2339631
    Abrahao, Bruno ; Soundarajan, Sucheta ; Hopcroft, John ; Kleinberg, Robert. / On the separability of structural classes of communities. KDD'12 - 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2012. pp. 624-632 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).
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