コミュニティ抽出のための新たな3部モジュラリティ

DOI
  • MURATA Tsuyoshi
    Dept. of Computer Science, Graduate School of Information Science Engineering, Tokyo Institute of Technology

書誌事項

タイトル別名
  • A New Tripartite Modularity for Detecting Communities

抄録

Many users are attracted by online social media such as Delicious and Digg, and they put tags on online resources. Relations among users, tags, and resources are represented as a tripartite network composed of three types of vertices. Detecting communities (densely connected subnetworks) from such tripartite networks is important for finding similar users, tags, and resources. For unipartite networks, several attempts have been made for detecting communities, and one of the popular approaches is to optimize modularity, a measurement for evaluating the goodness of network divisions. Modularity for bipartite networks is proposed by Barber, Guimera, Murata and Suzuki. However, as far as the author knows, there is few attempt for defining modularity for tripartite networks. This paper defines a new tripartite modularity which indicates the correspondence between communities of three vertex types. By optimizing the value of our tripartite modularity, better community structures can be detected from synthetic tripartite networks.

収録刊行物

詳細情報 詳細情報について

  • CRID
    1390282679715131904
  • NII論文ID
    130004892157
  • DOI
    10.11309/jssst.28.1_154
  • ISSN
    02896540
  • データソース種別
    • JaLC
    • CiNii Articles
  • 抄録ライセンスフラグ
    使用不可

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