Visual mining of multi-modal social networks at different abstraction levels

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

Research output: Contribution to journalConference articlepeer-review

31 Scopus citations

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. Invento 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.

Original languageEnglish (US)
Article number4272051
Pages (from-to)672-679
Number of pages8
JournalProceedings of the International Conference on Information Visualisation
DOIs
StatePublished - 2007
Event11th International Conference Information Visualization, IV 2007 - Zurich, Switzerland
Duration: Jul 4 2007Jul 6 2007

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition

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