Graph visualization with latent variable models
Proceedings of the Eighth Workshop on Mining and Learning with Graphs
Improving layered graph layouts with edge bundling
GD'10 Proceedings of the 18th international conference on Graph drawing
TGI-EB: a new framework for edge bundling integrating topology, geometry and importance
GD'11 Proceedings of the 19th international conference on Graph Drawing
Edge routing with ordered bundles
GD'11 Proceedings of the 19th international conference on Graph Drawing
Exploring the design space of interactive link curvature in network diagrams
Proceedings of the International Working Conference on Advanced Visual Interfaces
An empirical study on the impact of edge bundling on user comprehension of graphs
Proceedings of the International Working Conference on Advanced Visual Interfaces
Graph Bundling by Kernel Density Estimation
Computer Graphics Forum
Mad at edge crossings? break the edges!
FUN'12 Proceedings of the 6th international conference on Fun with Algorithms
Image-based edge bundles: simplified visualization of large graphs
EuroVis'10 Proceedings of the 12th Eurographics / IEEE - VGTC conference on Visualization
Winding roads: routing edges into bundles
EuroVis'10 Proceedings of the 12th Eurographics / IEEE - VGTC conference on Visualization
Force-directed edge bundling for graph visualization
EuroVis'09 Proceedings of the 11th Eurographics / IEEE - VGTC conference on Visualization
Visual recommendations for network navigation
EuroVis'11 Proceedings of the 13th Eurographics / IEEE - VGTC conference on Visualization
StreamEB: stream edge bundling
GD'12 Proceedings of the 20th international conference on Graph Drawing
Time-space varying visual analysis of micro-blog sentiment
Proceedings of the 6th International Symposium on Visual Information Communication and Interaction
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Graphs have been widely used to model relationships among data. For large graphs, excessive edge crossings make the display visually cluttered and thus difficult 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, flexible, and efficient. The experiments on some large graphs demonstrate the effectiveness of our method.