Consistent estimation of mixed memberships with successive projections

Maxim Panov, Konstantin Slavnov, Roman Ushakov

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    8 Citations (Scopus)

    Abstract

    This paper considers the parameter estimation problem in Mixed Membership Stochastic Block Model (MMSB), which is a quite general instance of random graph model allowing for overlapping community structure. We present the new algorithm successive projection overlapping clustering (SPOC) which combines the ideas of spectral clustering and geometric approach for separable non-negative matrix factorization. The proposed algorithm is provably consistent under MMSB with general conditions on the parameters of the model. SPOC is also shown to perform well experimentally in comparison to other algorithms.

    Original languageEnglish
    Title of host publicationComplex Networks and Their Applications VI - Proceedings of Complex Networks 2017 (The 6th International Conference on Complex Networks and Their Applications)
    EditorsHocine Cherifi, Chantal Cherifi, Mirco Musolesi, Márton Karsai
    PublisherSpringer Verlag
    Pages53-64
    Number of pages12
    ISBN (Print)9783319721491
    DOIs
    Publication statusPublished - 2018
    Event6th International Conference on Complex Networks and Their Applications, Complex Networks 2017 - Lyon, France
    Duration: 29 Nov 20171 Dec 2017

    Publication series

    NameStudies in Computational Intelligence
    Volume689
    ISSN (Print)1860-949X

    Conference

    Conference6th International Conference on Complex Networks and Their Applications, Complex Networks 2017
    Country/TerritoryFrance
    CityLyon
    Period29/11/171/12/17

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