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
Link To Document