• DocumentCode
    3042304
  • Title

    Visual Mining of Multi-Modal Social Networks at Different Abstraction Levels

  • Author

    Singh, Lisa ; Beard, Mitchell ; Getoor, Lise ; Blake, M. Brian

  • Author_Institution
    Georgetown Univ., Washington
  • fYear
    2007
  • fDate
    4-6 July 2007
  • Firstpage
    672
  • Lastpage
    679
  • Abstract
    Social networks continue to become more and more feature rich. Using local and global structural properties and descriptive attributes are necessary for more sophisticated social network analysis and support for visual mining tasks. While a number of visualization tools for social network applications have been developed, most of them are limited to uni-modal graph representations. Some of the tools support a wide range of visualization options, including interactive views. Others have better support for calculating structural graph properties such as the density of the graph or deploying traditional statistical social network analysis. We present Invenio, a new tool for visual mining of socials. Invenio integrates a wide range of interactive visualization options from Prefuse, with graph mining algorithm support from JUNG. While the integration expands the breadth of functionality within the core engine of the tool, our goal is to interactively explore multi-modal, multi-relational social networks. Invenio also supports construction of views using both database operations and basic graph mining operations.
  • Keywords
    data mining; data visualisation; database management systems; graph theory; interactive systems; social sciences computing; database; interactive visualization; multimodal social network; structural graph property; visual mining;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Visualization, 2007. IV '07. 11th International Conference
  • Conference_Location
    Zurich
  • ISSN
    1550-6037
  • Print_ISBN
    0-7695-2900-3
  • Type

    conf

  • DOI
    10.1109/IV.2007.126
  • Filename
    4272051