• DocumentCode
    2757000
  • Title

    Extending the classification of nodes in social networks

  • Author

    Heatherly, Raymond ; Kantarcioglu, Murat

  • Author_Institution
    Dept. of Comput. Sci., Univ. of Texas at Dallas, Dallas, TX, USA
  • fYear
    2011
  • fDate
    10-12 July 2011
  • Firstpage
    77
  • Lastpage
    82
  • Abstract
    Because of computational concerns, social network analysis generally uses only directly connected nodes to perform classification tasks. However, recent research indicates that this method of classification may not consider that nodes in the graph could have different influence over other nodes near them in the graph. It is possible that well-selected nodes may have a stronger importance in a social graph. Here, we analyze methods by which these important nodes may be identified and used to improve the classification of nodes within the social graph. We also show the effect of incorporating these important nodes in social network classification.
  • Keywords
    graph theory; pattern classification; social networking (online); directly connected nodes; graph nodes; node classification; social graph; social network analysis; Artificial neural networks; Convergence; Industries; Reflection; Social network services; Terrorism; Training;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligence and Security Informatics (ISI), 2011 IEEE International Conference on
  • Conference_Location
    Beijing
  • Print_ISBN
    978-1-4577-0082-8
  • Type

    conf

  • DOI
    10.1109/ISI.2011.5984054
  • Filename
    5984054