• DocumentCode
    116470
  • Title

    Empirical study on overlapping community detection in question and answer sites

  • Author

    Meng, Zhou ; Gandon, Fabien ; Zucker, Catherine Faron ; Ge Song

  • Author_Institution
    INRIA Sophia Antipolis Mediterranee, Sophia Antipolis, France
  • fYear
    2014
  • fDate
    17-20 Aug. 2014
  • Firstpage
    344
  • Lastpage
    348
  • Abstract
    In many social networks, people interact based on their interests. Community detection algorithms are then useful to reveal the sub-structures of a network and help us find interest groups. Identifying these social communities can bring benefit to understanding and predicting users behaviors. However, for some kind of online community sites such as question-and-answer (Q&A) sites or forums, there is no friendship based social network structure, which means people are not aware who they are in contact with. Therefore, many traditional community detection techniques do not apply directly. In this paper, we propose an empirical approach for extracting data from Q&A sites suitable to apply community detection methods. Then we compare three kinds of community detection methods we applied on a dataset extracted from the popular Q&A site StackOverflow. We analyze and comment the results of each method.
  • Keywords
    information retrieval; social networking (online); Q&A sites; community detection algorithms; online community sites; overlapping community detection; question and answer sites; social communities; social network structure; Cascading style sheets; Clustering algorithms; Communities; HTML; Layout; Social network services; Vegetation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Advances in Social Networks Analysis and Mining (ASONAM), 2014 IEEE/ACM International Conference on
  • Conference_Location
    Beijing
  • Type

    conf

  • DOI
    10.1109/ASONAM.2014.6921608
  • Filename
    6921608