A separability framework for analyzing community structure

Bruno Abrahao, Sucheta Soundarajan, John Hopcroft, Robert Kleinberg

    Research output: Contribution to journalArticle

    Abstract

    Four major factors govern the intricacies of community extraction in networks: (1) the literature offers a multitude of disparate community detection algorithms whose output exhibits high structural variability across the collection, (2) communities identified by algorithms may differ structurally from real communities that arise in practice, (3) there is no consensus characterizing how to discriminate communities from noncommunities, and (4) the application domain includes a wide variety of networks of fundamentally different natures. In this article, we present a class separability framework to tackle these challenges through a comprehensive analysis of community properties. Our approach enables the assessment of the structural dissimilarity among the output of multiple community detection algorithms and between the output of algorithms and communities that arise in practice. In addition, our method provides us with a way to organize the vast collection of community detection algorithms by grouping those that behave similarly. Finally, we identify themost discriminative graph-theoretical properties of community signature and the small subset of properties that account for most of the biases of the different community detection algorithms. We illustrate our approach with an experimental analysis, which reveals nuances of the structure of real and extracted communities. In our experiments, we furnish our framework with the output of 10 different community detection procedures, representative of categories of popular algorithms available in the literature, applied to a diverse collection of large-scale real network datasets whose domains span biology, online shopping, and social systems. We also analyze communities identified by annotations that accompany the data, which reflect exemplar communities in various domain.We characterize these communities using a broad spectrum of community properties to produce the different structural classes. As our experiments show that community structure is not a universal concept, our framework enables an informed choice of the most suitable community detection method for identifying communities of a specific type in a given network and allows for a comparison of existing community detection algorithms while guiding the design of new ones.

    Original languageEnglish (US)
    Article number2527231
    JournalACM Transactions on Knowledge Discovery from Data
    Volume8
    Issue number1
    DOIs
    StatePublished - Jan 1 2014

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    Experiments

    Keywords

    • Class separability
    • Community structure
    • Detection algorithms
    • Networks

    ASJC Scopus subject areas

    • Computer Science(all)

    Cite this

    A separability framework for analyzing community structure. / Abrahao, Bruno; Soundarajan, Sucheta; Hopcroft, John; Kleinberg, Robert.

    In: ACM Transactions on Knowledge Discovery from Data, Vol. 8, No. 1, 2527231, 01.01.2014.

    Research output: Contribution to journalArticle

    Abrahao, Bruno ; Soundarajan, Sucheta ; Hopcroft, John ; Kleinberg, Robert. / A separability framework for analyzing community structure. In: ACM Transactions on Knowledge Discovery from Data. 2014 ; Vol. 8, No. 1.
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