Force-transfer: a new approach to removing overlapping nodes in graph layout
ACSC '03 Proceedings of the 26th Australasian computer science conference - Volume 16
A framework of filtering, clustering and dynamic layout graphs for visualization
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
A new algorithm for removing node overlapping in graph visualization
Information Sciences: an International Journal
Removing Node Overlaps Using Multi-sphere Scheme
Graph Drawing
Multi-con: exploring graphs by fast switching among multiple contexts
Proceedings of the International Conference on Advanced Visual Interfaces
Hi-tree layout using quadratic programming
Diagrams'10 Proceedings of the 6th international conference on Diagrammatic representation and inference
GD'05 Proceedings of the 13th international conference on Graph Drawing
Rolled-out Wordles: A Heuristic Method for Overlap Removal of 2D Data Representatives
Computer Graphics Forum
Visual access to graph content using magic lenses and filtering
Proceedings of the 28th Spring Conference on Computer Graphics
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Although graph drawing has been extensively studied, little attention has been paid to the problem of node overlapping. The problem arises because almost all existing graph layout algorithms assume that nodes are points. In practice, however, nodes may be labelled, and these labels may overlap. Here we investigate how such node overlapping can be removed in a subsequent layout adjustment phase. We propose four different approaches for removing node overlapping, all of which are based on constrained optimization techniques. The first is the simplest. It performs the minimal linear scaling which will remove node-overlapping. The second approach relies on formulating the node overlapping problem as a convex quadratic programming problem, which can then be solved by any quadratic solver. The disadvantage is that, since constraints must be linear, the node overlapping constraints cannot be expressed directly, but must be strengthened to obtain a linear constraint strong enough to ensure no node overlapping. The third and fourth approaches are based on local search methods. The third is an adaptation of the EGENET solver originally designed for solving general constraint satisfaction problems, while the fourth approach is a form of Lagrangian multiplier method, a well-known optimization technique used in operations research. Both the third and fourth method are able to handle the node overlapping constraints directly, and thus may potentially find better solutions. Their disadvantage is that no efficient global optimization methods are available for such problems, and hence we must accept a local minimum. We illustrate all of the above methods on a series of layout adjustment problems.