Authors
Weiwei Cui, the Hong Kong University of Science and Technology
Hong Zhou, the Hong Kong University of Science and Technology
Huamin Qu, the Hong Kong University of Science and Technology
Pak Chung Wong, Pacific Northwest National Laboratory
Xiaoming Li, Peking University
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TVCG.2008.135
Download - Video | Audio | Slides
Running Time: 29 min
Abstract
Graphs have been widely used to model relationships among data. For large graphs, excessive edge crossings make the display visually cluttered and thus dif?cult to explore. In this paper, we propose a novel geometry-based edge-clustering framework that can group edges into bundles to reduce the overall edge crossings. Our method uses a control mesh to guide the edge-clustering process; edge bundles can be formed by forcing all edges to pass through some control points on the mesh. The control mesh can be generated at different levels of detail either manually or automatically based on underlying graph patterns. Users can further interact with the edge-clustering results through several advanced visualization techniques such as color and opacity enhancement. Compared with other edge-clustering methods, our approach is intuitive, ?exible, and ef?cient. The experiments on some large graphs demonstrate the effectiveness of our method.
Citation
Weiwei Cui, Hong Zhou, Huamin Qu, Pak Chung Wong, Xiaoming Li, “Geometry-Based Edge Clustering for Graph Visualization,” IEEE Transactions on Visualization and Computer Graphics, vol. 14, no. 6, pp. 1277-1284, Nov/Dec, 2008