Journal of the ACM (JACM)
A Comparison of the Readability of Graphs Using Node-Link and Matrix-Based Representations
INFOVIS '04 Proceedings of the IEEE Symposium on Information Visualization
Interactive Visualization of Small World Graphs
INFOVIS '04 Proceedings of the IEEE Symposium on Information Visualization
INFOVIS '05 Proceedings of the Proceedings of the 2005 IEEE Symposium on Information Visualization
Hierarchical Edge Bundles: Visualization of Adjacency Relations in Hierarchical Data
IEEE Transactions on Visualization and Computer Graphics
Visualizing evolving networks: minimum spanning trees versus pathfinder networks
INFOVIS'03 Proceedings of the Ninth annual IEEE conference on Information visualization
Multiscale visualization of small world networks
INFOVIS'03 Proceedings of the Ninth annual IEEE conference on Information visualization
Using multilevel call matrices in large software projects
INFOVIS'03 Proceedings of the Ninth annual IEEE conference on Information visualization
An experimental comparison of fast algorithms for drawing general large graphs
GD'05 Proceedings of the 13th international conference on Graph Drawing
Energy-based clustering of graphs with nonuniform degrees
GD'05 Proceedings of the 13th international conference on Graph Drawing
Image-based edge bundles: simplified visualization of large graphs
EuroVis'10 Proceedings of the 12th Eurographics / IEEE - VGTC conference on Visualization
Planar preprocessing for spring embedders
GD'12 Proceedings of the 20th international conference on Graph Drawing
Visual analysis of large-scale network anomalies
IBM Journal of Research and Development
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Current graph drawing algorithms enable the creation of two dimensional node-link diagrams of huge graphs. However, for graphs with low diameter (of which "small world" graphs are a subset) these techniques begin to break down visually even when the graph has only a few hundred nodes. Typical algorithms produce images where nodes clump together in the center of the screen, making it hard to discern structure and follow paths. This paper describes a solution to this problem, which uses a global edge metric to determine a subset of edges that capture the graph's intrinsic clustering structure. This structure is then used to create an embedding of the graph, after which the remaining edges are added back in. We demonstrate applications of this technique to a number of real world examples.