• DocumentCode
    3155225
  • Title

    A Method for Local Community Detection by Finding Core Nodes

  • Author

    Tiantian Zhang ; Bin Wu

  • Author_Institution
    Sch. of Comput., Beijing Univ. of Posts & Telecommun., Beijing, China
  • fYear
    2012
  • fDate
    26-29 Aug. 2012
  • Firstpage
    1171
  • Lastpage
    1176
  • Abstract
    Currently, the detection of global community structure in networks has gathered a lot of attention. Most of the methods need global knowledge of the graphs which would be unrealistic to get when the graphs are too large or evolve too quickly. Moreover, sometimes we are only interested in the community structures of some given nodes, not all nodes. So detecting the community of a given node i.e. local community detection is more appropriate. Most of the proposed solutions for local community detection built upon the source nodes are sensitive to the position of source nodes. In this paper, we propose a method to detect local community of a given node by finding the core node of the community firstly. Then expand the core node´s cliques to get community of the given node. We validate our method on real-world networks whose community structures are available. The result shows that our method can get high recall and precision score and is quite effective and flexible to identify local communities, irrespective of the source node position.
  • Keywords
    graph theory; network theory (graphs); core node clique expansion; core node finding; global community structure detection; graph global knowledge; local community detection; precision score; real-world networks; recall score; source nodes; Blogs; Communities; Educational institutions; Extraterrestrial measurements; Image edge detection; Knowledge engineering; Social network services; Local community detection; clique; core node; strength of relattion;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Advances in Social Networks Analysis and Mining (ASONAM), 2012 IEEE/ACM International Conference on
  • Conference_Location
    Istanbul
  • Print_ISBN
    978-1-4673-2497-7
  • Type

    conf

  • DOI
    10.1109/ASONAM.2012.202
  • Filename
    6425598