Computational geometry: an introduction
Computational geometry: an introduction
Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
Data Structures and Algorithm Analysis in Java
Data Structures and Algorithm Analysis in Java
Graph Drawing: Algorithms for the Visualization of Graphs
Graph Drawing: Algorithms for the Visualization of Graphs
Removing edge-node intersections in drawings of graphs
Information Processing Letters
A Layout Adjustment Problem for Disjoint Rectangles Preserving Orthogonal Order
GD '98 Proceedings of the 6th International Symposium on Graph Drawing
Improved Force-Directed Layouts
GD '98 Proceedings of the 6th International Symposium on Graph Drawing
Cluster busting in anchored graph drawing
CASCON '92 Proceedings of the 1992 conference of the Centre for Advanced Studies on Collaborative research - Volume 2
Flexible layering in hierarchical drawings with nodes of arbitrary size
ACSC '04 Proceedings of the 27th Australasian conference on Computer science - Volume 26
ACSC '05 Proceedings of the Twenty-eighth Australasian conference on Computer Science - Volume 38
Using spring algorithms to remove node overlapping
APVis '05 proceedings of the 2005 Asia-Pacific symposium on Information visualisation - Volume 45
Drawing graphs with non-uniform vertices
Proceedings of the Working Conference on Advanced Visual Interfaces
IPSep-CoLa: An Incremental Procedure for Separation Constraint Layout of Graphs
IEEE Transactions on Visualization and Computer Graphics
TopoLayout: Multilevel Graph Layout by Topological Features
IEEE Transactions on Visualization and Computer Graphics
Satisficing scrolls: a shortcut to satisfactory layout
Proceedings of the eighth ACM symposium on Document engineering
Removing Node Overlaps Using Multi-sphere Scheme
Graph Drawing
Drawing graphs with nonuniform nodes using potential fields
Journal of Visual Languages and Computing
Integrating edge routing into force-directed layout
GD'06 Proceedings of the 14th international conference on Graph drawing
Fast node overlap removal: correction
GD'06 Proceedings of the 14th international conference on Graph drawing
Interactive searching and visualization of patterns in attributed graphs
Proceedings of Graphics Interface 2010
Hi-tree layout using quadratic programming
Diagrams'10 Proceedings of the 6th international conference on Diagrammatic representation and inference
Fast edge-routing for large graphs
GD'09 Proceedings of the 17th international conference on Graph Drawing
GD'09 Proceedings of the 17th international conference on Graph Drawing
Layout with circular and other non-linear constraints using procrustes projection
GD'09 Proceedings of the 17th international conference on Graph Drawing
Rolled-out Wordles: A Heuristic Method for Overlap Removal of 2D Data Representatives
Computer Graphics Forum
Grouse: feature-based, steerable graph hierarchy exploration
EUROVIS'07 Proceedings of the 9th Joint Eurographics / IEEE VGTC conference on Visualization
Visual access to graph content using magic lenses and filtering
Proceedings of the 28th Spring Conference on Computer Graphics
Visualizing streaming text data with dynamic graphs and maps
GD'12 Proceedings of the 20th international conference on Graph Drawing
Domain specific vs generic network visualization: an evaluation with metabolic networks
AUIC '11 Proceedings of the Twelfth Australasian User Interface Conference - Volume 117
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The problem of node overlap removal is to adjust the layout generated by typical graph drawing methods so that nodes of non-zero width and height do not overlap, yet are as close as possible to their original positions. We give an O(n log n) algorithm for achieving this assuming that the number of nodes overlapping any single node is bounded by some constant. This method has two parts, a constraint generation algorithm which generates a linear number of “separation” constraints and an algorithm for finding a solution to these constraints “close” to the original node placement values. We also extend our constraint solving algorithm to give an active set based algorithm which is guaranteed to find the optimal solution but which has considerably worse theoretical complexity. We compare our method with convex quadratic optimization and force scan approaches and find that it is faster than either, gives results of better quality than force scan methods and similar quality to the quadratic optimisation approach.