Abstract
The hierarchical edge bundle (HEB) method generates useful visualizations of dense graphs, such as social networks, but requires a predefined clustering hierarchy, and does not easily benefit from existing straight-line visualization improvements. This paper proposes a new clustering approach that extracts the community structure of a network and organizes it into a hierarchy that is flatter than existing community-based clustering approaches and maps better to HEB visualization. Our method not only discovers communities and generates clusters with better modularization qualities, but also creates a balanced hierarchy that allows HEB visualization of unstructured social networks without predefined hierarchies. Results on several data sets demonstrate that this approach clarifies real-world communication, collaboration and competition network structure and reveals information missed in previous visualizations. We further implemented our techniques into a social network visualization application on facebook.com and let users explore the visualization and community clustering of their own social networks.
Original language | English (US) |
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Pages (from-to) | 2314-2327 |
Number of pages | 14 |
Journal | Computer Graphics Forum |
Volume | 30 |
Issue number | 8 |
DOIs | |
State | Published - Dec 2011 |
Keywords
- Betweenness centrality
- Edge bundles
- Network clustering
- Visualization
ASJC Scopus subject areas
- Computer Graphics and Computer-Aided Design