Georgetown University LogoGeorgetown University Library LogoDigitalGeorgetown Home
    • Login
    View Item 
    •   DigitalGeorgetown Home
    • Georgetown University Institutional Repository
    • Georgetown College
    • Department of Computer Science
    • Faculty Scholarship - Computer Science Department
    • View Item
    •   DigitalGeorgetown Home
    • Georgetown University Institutional Repository
    • Georgetown College
    • Department of Computer Science
    • Faculty Scholarship - Computer Science Department
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Exploring community structure in biological networks with random graphs

    Cover for Exploring community structure in biological networks with random graphs
    View/Open
    View/Open: ExploringCommunityStructure.pdf (18.MB) Bookview

    Creator
    Sah, Pratha
    Singh, Lisa
    Clauset, Aaron
    Bansal, Shweta
    Abstract
    Community structure is ubiquitous in biological networks. There has been an increased interest in unraveling the community structure of biological systems as it may provide important insights into a system’s functional components and the impact of local structures on dynamics at a global scale. Choosing an appropriate community detection algorithm to identify the community structure in an empirical network can be difficult, however, as the many algorithms available are based on a variety of cost functions and are difficult to validate. Even when community structure is identified in an empirical system, disentangling the effect of community structure from other network properties such as clustering coefficient and assortativity can be a challenge.
    Permanent Link
    http://hdl.handle.net/10822/761516
    Date Published
    2014
    Subject
    Computer Science; Biological networks; Bioinformatics;
    Type
    text
    Publisher
    BioMed Central
    Collections
    • Faculty Scholarship - Computer Science Department
    Metadata
    Show full item record

    Related items

    Showing items related by title, author, creator and subject.

    • Cover for Exploring graph mining approaches for dynamic heterogeneous networks

      Exploring graph mining approaches for dynamic heterogeneous networks 

      Singh, Lisa (2007)
      As we become a more 'connected' society, a greater need exists to understand complex network structures. While many in the field of data mining analyze network data, most models of networks are straightforward-focusing on ...
    Related Items in Google Scholar

    Georgetown University Seal
    ©2009 - 2023 Georgetown University Library
    37th & O Streets NW
    Washington DC 20057-1174
    202.687.7385
    digitalscholarship@georgetown.edu
    Accessibility
     

     

    Browse

    All of DigitalGeorgetownCommunities & CollectionsCreatorsTitlesBy Creation DateThis CollectionCreatorsTitlesBy Creation Date

    My Account

    Login

    Statistics

    View Usage Statistics

    Georgetown University Seal
    ©2009 - 2023 Georgetown University Library
    37th & O Streets NW
    Washington DC 20057-1174
    202.687.7385
    digitalscholarship@georgetown.edu
    Accessibility