• DocumentCode
    249294
  • Title

    Rectangle Counting in Large Bipartite Graphs

  • Author

    Jia Wang ; Fu, Ada Wai-Chee ; Cheng, James

  • Author_Institution
    Dept. of Comput. Sci. & Eng., Chinese Univ. of Hong Kong, Hong Kong, China
  • fYear
    2014
  • fDate
    June 27 2014-July 2 2014
  • Firstpage
    17
  • Lastpage
    24
  • Abstract
    Rectangles are the smallest cycles (i.e., cycles of length 4) and most elementary sub-structures in a bipartite graph. Similar to triangle counting in uni-partite graphs, rectangle counting has many important applications where data is modeled as bipartite graphs. However, efficient algorithms for rectangle counting are lacking. We propose three different types of algorithms to cope with different data volumes and the availability of computing resources. We verified the efficiency of our algorithms with experiments on both large real-world and synthetic bipartite graphs.
  • Keywords
    computational complexity; graph theory; bipartite graph substructures; computing resource availability; cycle length; data modelling; data volumes; large-real-world bipartite graphs; large-synthetic bipartite graphs; rectangle counting; time complexity; Algorithm design and analysis; Bipartite graph; Clustering algorithms; Complexity theory; Equations; Parallel algorithms; Partitioning algorithms; bipartite graphs; rectangle counting;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Big Data (BigData Congress), 2014 IEEE International Congress on
  • Conference_Location
    Anchorage, AK
  • Print_ISBN
    978-1-4799-5056-0
  • Type

    conf

  • DOI
    10.1109/BigData.Congress.2014.13
  • Filename
    6906756