• Login
    View Item 
    •   Home
    • Research
    • Articles
    • View Item
    •   Home
    • Research
    • Articles
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of KAUSTCommunitiesIssue DateSubmit DateThis CollectionIssue DateSubmit Date

    My Account

    Login

    Quick Links

    Open Access PolicyORCID LibguideTheses and Dissertations LibguideSubmit an Item

    Statistics

    Display statistics

    A scalable community detection algorithm for large graphs using stochastic block models

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    A scalable community detection algorithm for large graphs using stochastic block models.pdf
    Size:
    969.9Kb
    Format:
    PDF
    Description:
    Accepted Manuscript
    Download
    Type
    Article
    Authors
    Peng, Chengbin cc
    Zhang, Zhihua
    Wong, Ka-Chun
    Zhang, Xiangliang cc
    Keyes, David E. cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Computer Science Program
    Applied Mathematics and Computational Science Program
    Extreme Computing Research Center
    Date
    2017-11-24
    Permanent link to this record
    http://hdl.handle.net/10754/626776
    
    Metadata
    Show full item record
    Abstract
    Community detection in graphs is widely used in social and biological networks, and the stochastic block model is a powerful probabilistic tool for describing graphs with community structures. However, in the era of
    Citation
    Chengbin Peng, Zhihua Zhang, Ka-Chun Wong, Xiangliang Zhang, David E. Keyes. A scalable community detection algorithm for large graphs using stochastic block models. IDA. IOS Press; 2017;21: 1463–1485. doi:10.3233/IDA-163156
    Sponsors
    Research reported in this publication was supported by King Abdullah University of Science and Technology (KAUST).
    Publisher
    IOS Press
    Journal
    Intelligent Data Analysis
    DOI
    10.3233/IDA-163156
    Additional Links
    https://content.iospress.com/articles/intelligent-data-analysis/ida163156
    https://www.ijcai.org/Proceedings/15/Papers/296.pdf
    ae974a485f413a2113503eed53cd6c53
    10.3233/IDA-163156
    Scopus Count
    Collections
    Articles; Applied Mathematics and Computational Science Program; Extreme Computing Research Center; Computer Science Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

    entitlement

     
    DSpace software copyright © 2002-2023  DuraSpace
    Quick Guide | Contact Us | KAUST University Library
    Open Repository is a service hosted by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items. For anonymous users the allowed maximum amount is 50 search results.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.