Subspace based network community detection using sparse linear coding

Arif Mahmood, Michael Small

    Research output: Chapter in Book/Conference paperConference paperpeer-review

    7 Citations (Scopus)


    Information mining from networks by identifying communities is an important problem across a number of research fields including social science, biology, physics, and medicine. Most existing community detection algorithms are graph theoretic and lack the ability to detect accurate community boundaries if the ratio of intra-community to inter-community links is low. Also, algorithms based on modularity maximization may fail to resolve communities smaller than a specific size if the community size varies significantly. We propose a fundamentally different community detection algorithm based on the fact that each network community spans a different subspace in the geodesic space. Therefore, each node can only be efficiently represented as a linear combination of nodes spanning the same subspace (Fig. 1). To make the process of community detection more robust, we use sparse linear coding with l1 norm constraint. In order to find a community label for each node, sparse spectral clustering algorithm is used. The proposed community detection technique is compared with more than ten state of the art methods on two benchmark networks (with known clusters) using normalized mutual information criterion. Our proposed algorithm outperformed existing methods with a significant margin on both benchmark networks.
    Original languageEnglish
    Title of host publication2016 IEEE 32nd International Conference on Data Engineering (ICDE)
    PublisherIEEE, Institute of Electrical and Electronics Engineers
    Number of pages2
    ISBN (Electronic)978-1-5090-2020-1
    Publication statusPublished - 2016
    Event2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016 - Helsinki, Finland
    Duration: 16 May 201620 May 2016


    Conference2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016


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