An algorithm for drawing general undirected graphs
Information Processing Letters
Graph drawing by force-directed placement
Software—Practice & Experience
Graph Drawing: Algorithms for the Visualization of Graphs
Graph Drawing: Algorithms for the Visualization of Graphs
FADE: Graph Drawing, Clustering, and Visual Abstraction
GD '00 Proceedings of the 8th International Symposium on Graph Drawing
A Multilevel Algorithm for Force-Directed Graph Drawing
GD '00 Proceedings of the 8th International Symposium on Graph Drawing
Disconnected Graph Layout and the Polyomino Packing Approach
GD '01 Revised Papers from the 9th International Symposium on Graph Drawing
Topological Fisheye Views for Visualizing Large Graphs
IEEE Transactions on Visualization and Computer Graphics
Graph drawing by stress majorization
GD'04 Proceedings of the 12th international conference on Graph Drawing
Drawing large graphs with a potential-field-based multilevel algorithm
GD'04 Proceedings of the 12th international conference on Graph Drawing
Graph drawing by subspace optimization
VISSYM'04 Proceedings of the Sixth Joint Eurographics - IEEE TCVG conference on Visualization
CET: a tool for creative exploration of graphs
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
GD'09 Proceedings of the 17th international conference on Graph Drawing
On the integration of graph exploration and data analysis: the creative exploration toolkit
Bisociative Knowledge Discovery
Stress functions for nonlinear dimension reduction, proximity analysis, and graph drawing
The Journal of Machine Learning Research
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We introduce a new force-directed model for computing graph layout. The model bridges the two more popular force directed approaches --- the stress and the electrical-spring models --- through the binary stress cost function, which is a carefully defined energy function with low descriptive complexity allowing fast computation via a Barnes-Hut scheme. This allows us to overcome optimization pitfalls from which previous methods suffer. In addition, the binary stress model often offers a unique viewpoint to the graph, which can occasionally add useful insight to its topology. The model uniformly spreads the nodes within a circle. This helps in achieving an efficient utilization of the drawing area. Moreover, the ability to uniformly spread nodes regardless of topology, becomes particularly helpful for graphs with low connectivity, or even with multiple connected components, where there is not enough structure for defining a readable layout.