• DocumentCode
    3040892
  • Title

    An Efficient Algorithm for Overlapping Community Detection in Complex Networks

  • Author

    Chen, Duanbing ; Fu, Yan ; Shang, Mingsheng

  • Author_Institution
    Sch. of Comput. Sci., Univ. of Electron. Sci. & Technol. of China, Chengdu, China
  • Volume
    1
  • fYear
    2009
  • fDate
    19-21 May 2009
  • Firstpage
    244
  • Lastpage
    247
  • Abstract
    Community structure is an important property of the complex networks. How to detect the communities is significant to understand the network structure and analyze the network properties. Many algorithms, such as K-L and GN, have been proposed to detect the community structure in complex networks. However, the communities detected by these algorithms are always not overlapping. According to daily experience, a node in the network may belong to several communities and a community should have many nodes and connections. Based on these principals and existing researches, an efficient algorithm for overlapping community detection in complex networks is proposed in this paper. The key strategy of the algorithm is to mine a node with the closest relations with the community and assign it to the community. Some real-world networks are used to test the performance of the algorithm. Experimental results demonstrate that the algorithm proposed is rather efficient to detect the overlapping community in complex networks.
  • Keywords
    complex networks; network theory (graphs); community detection; community structure; complex network; efficient algorithm; network properties; network structure; Clustering algorithms; Complex networks; Computer science; Intelligent networks; Intelligent structures; Intelligent systems; Laplace equations; Partitioning algorithms; Sparse matrices; Testing; algorithm; complex networks; detection; overlapping community;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Systems, 2009. GCIS '09. WRI Global Congress on
  • Conference_Location
    Xiamen
  • Print_ISBN
    978-0-7695-3571-5
  • Type

    conf

  • DOI
    10.1109/GCIS.2009.68
  • Filename
    5208980