• DocumentCode
    3585330
  • Title

    Identifying Influential Nodes in Bipartite Networks Using the Clustering Coefficient

  • Author

    Liebig, Jessica ; Rao, Asha

  • Author_Institution
    Sch. of Math. & Geospatial Sci., RMIT Univ., Melbourne, VIC, Australia
  • fYear
    2014
  • Firstpage
    323
  • Lastpage
    330
  • Abstract
    The identification of influential nodes in complex network can be very challenging. If the network has a community structure, centrality measures may fail to identify the complete set of influential nodes, as the hubs and other central nodes of the network may lie inside only one community. Here we define a bipartite clustering coefficient that, by taking differently structured clusters into account, can find important nodes across communities.
  • Keywords
    complex networks; network theory (graphs); pattern clustering; bipartite clustering coefficient; bipartite network; centrality measure; community structure; complex network; influential node; Collaboration; Communities; Conferences; Geospatial analysis; Joining processes; Length measurement; Loss measurement;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Signal-Image Technology and Internet-Based Systems (SITIS), 2014 Tenth International Conference on
  • Type

    conf

  • DOI
    10.1109/SITIS.2014.15
  • Filename
    7081566