Winding Roads: Routing edges into bundles
Visualizing graphs containing many nodes and edges efficiently is quite challenging. Drawings of such graphs generally suffer from visual clutter induced by the large amount of edges and their crossings. Consequently it is difficult to read the relationships between nodes and the high‐level edge pat...
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| Vydáno v: | Computer graphics forum Ročník 29; číslo 3; s. 853 - 862 |
|---|---|
| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Oxford, UK
Blackwell Publishing Ltd
01.06.2010
Wiley |
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| ISSN: | 0167-7055, 1467-8659 |
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| Abstract | Visualizing graphs containing many nodes and edges efficiently is quite challenging. Drawings of such graphs generally suffer from visual clutter induced by the large amount of edges and their crossings. Consequently it is difficult to read the relationships between nodes and the high‐level edge patterns that may exist in standard node‐link diagram representations. Edge bundling techniques have been proposed to help solve this issue, which rely on high quality edge rerouting. In this paper, we introduce an intuitive edge bundling technique which efficiently reduces edge clutter in graphs drawings. Our method is based on the use of a grid built using the original graph to compute the edge rerouting. In comparison with previously proposed edge bundling methods, our technique improves both the level of clutter reduction and the computation performance. The second contribution of this paper is a GPU‐based rendering method which helps users perceive bundles densities while preserving edge color. |
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| AbstractList | Visualizing graphs containing many nodes and edges efficiently is quite challenging. Drawings of such graphs generally suffer from visual clutter induced by the large amount of edges and their crossings. Consequently it is difficult to read the relationships between nodes and the high‐level edge patterns that may exist in standard node‐link diagram representations. Edge bundling techniques have been proposed to help solve this issue, which rely on high quality edge rerouting. In this paper, we introduce an intuitive edge bundling technique which efficiently reduces edge clutter in graphs drawings. Our method is based on the use of a grid built using the original graph to compute the edge rerouting. In comparison with previously proposed edge bundling methods, our technique improves both the level of clutter reduction and the computation performance. The second contribution of this paper is a GPU‐based rendering method which helps users perceive bundles densities while preserving edge color. AbstractVisualizing graphs containing many nodes and edges efficiently is quite challenging. Drawings of such graphs generally suffer from visual clutter induced by the large amount of edges and their crossings. Consequently it is difficult to read the relationships between nodes and the high-level edge patterns that may exist in standard node-link diagram representations. Edge bundling techniques have been proposed to help solve this issue, which rely on high quality edge rerouting. In this paper, we introduce an intuitive edge bundling technique which efficiently reduces edge clutter in graphs drawings. Our method is based on the use of a grid built using the original graph to compute the edge rerouting. In comparison with previously proposed edge bundling methods, our technique improves both the level of clutter reduction and the computation performance. The second contribution of this paper is a GPU-based rendering method which helps users perceive bundles densities while preserving edge color. |
| Author | Bourqui, R. Auber, D. Lambert, A. |
| Author_xml | – sequence: 1 givenname: A. surname: Lambert fullname: Lambert, A. organization: CNRS UMR5800 LaBRI, INRIA Bordeaux - Slid Quest, France – sequence: 2 givenname: R. surname: Bourqui fullname: Bourqui, R. organization: CNRS UMR5800 LaBRI, INRIA Bordeaux - Slid Quest, France – sequence: 3 givenname: D. surname: Auber fullname: Auber, D. organization: CNRS UMR5800 LaBRI, INRIA Bordeaux - Slid Quest, France |
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| Cites_doi | 10.1007/978-3-540-31843-9_29 10.1007/978-3-642-11805-0_15 10.1109/INFVIS.2005.1532150 10.1007/3-540-63938-1_68 10.1093/comjnl/10.1.85 10.1007/978-3-540-70904-6_37 10.1145/563858.563893 10.1109/TVCG.2008.135 10.1109/INFVIS.2004.43 10.1145/800248.507101 10.1145/10515.10549 10.1515/crll.1908.134.198 10.1007/11618058_40 10.1109/INFVIS.2004.47 10.1109/TVCG.2003.1196007 10.1109/PACIFICVIS.2008.4475459 10.1007/978-3-540-24595-7_1 10.1109/TVCG.2006.147 10.1007/BF01840435 10.1109/TVCG.2006.120 10.1109/TVCG.2007.70535 10.1145/1201775.882363 10.1007/BF00288933 10.1007/3-540-58950-3_393 |
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| References_xml | – reference: Abello J., Van Ham F., Krishnan N.: ASK-GraphView : A Large Graph Visualisation System. IEEE Transactions on Visualization and Computer Graphics 12, 5 (2006), 669-676. – reference: Gonzalez R. C., Woods R. E.: Digital Image Processing (3rd Edition). Prentice-Hall, Inc., Upper Saddle River , NJ , USA , 2006, ch. 10, pp. 707-710. – reference: Holten D., Van Wijk J. J.: Force-directed edge bundling for graph visualization. In 11th Eurographics/IEEE-VGTC Symposium on Visualization (Computer Graphics Forum; Proceedings of EuroVis 2009) (2009), vol. 31, pp. 983-990. – reference: Sedgewick R., Vitter J. S.: Shortest paths in euclidean graphs. Algorithmica 1 (nov 1986), 31-48. – reference: Welsh D. J., Powell M. B.: An upper bound to the chromaticnumber of a graph and its application to timetabling problems. The Computer journal 10 (1967), 85-86. – reference: Van Liere R., De Leeuw W.: GraphSplatting : Visualizing graphs as continuous fields. IEEE Transactions on Visualization and Computer Graphics 9, 2 (2003), 206-212. – reference: Voronoi G.: Nouveles applications des paramètres continus à la théorie de formas quadratiques. J Reine Angew Math 134 (1908), 198-287. – reference: Finkel R. A., Bentley J. L.: Quad trees a data structure for retrieval on composite keys. Acta Informatica 4, 1 (March 1974), 1-9. – reference: Szirmay-Kalos L., Umenhoffer T.: Displacement mapping on the GPU - State of the Art. Computer Graphics Forum 27, 1 (2008). – reference: Cui W., Zhou H., Qu H., Wong P. C., Li X.: Geometry-based edge clustering for graph visualization. IEEE Transactions on Visualization and Computer Graphics 14, 6 (2008), 1277-1284. – reference: Smith S. W.: The scientist and engineer's guide to digital signal processing. California Technical Publishing, San Diego , CA , USA , 1997, ch. 24, pp. 404-407. – reference: Willems N., Van de Wetering H., Van Wijk J. J.: Visualization of vessel movements. In 11th Eurographics/IEEE-VGTC Symposium on Visualization (Computer Graphics Forum; Proceedings of EuroVis 2009) (2009), vol. 31, pp. 959-966. – reference: Ellis G., Dix A.: A taxonomy of clutter reduction for information visualisation. IEEE Transactions on Visualization and Computer Graphics 13, 6 (2007), 1216-1223. – reference: Holten D.: Hierachical Edge Bundles : Visualization of Adjacency Relations in Hierarchical Data. 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| Snippet | Visualizing graphs containing many nodes and edges efficiently is quite challenging. Drawings of such graphs generally suffer from visual clutter induced by... AbstractVisualizing graphs containing many nodes and edges efficiently is quite challenging. Drawings of such graphs generally suffer from visual clutter... |
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| SubjectTerms | 1.3.3 [Computer Graphics]: Picture/Image Generation-Line and curve generation Bundles Bundling Clutter Computer graphics Computer Science Density Graph coloring Graphs Other Reduction Rendering Studies |
| Title | Winding Roads: Routing edges into bundles |
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