FADE: Graph Drawing, Clustering, and Visual Abstraction

  • Aaron Quigley
  • Peter Eades
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1984)


A fast algorithm(fade) for the 2D drawing, geometric clustering and multilevel viewing of large undirected graphs is presented. The algorithm is an extension of the Barnes-Hut hierarchical space decomposition method, which includes edges and multilevel visual abstraction. Compared to the original force directed algorithm, the time overhead is O(e + n log n) where n and e are the numbers of nodes and edges. The improvement is possible since the decomposition tree provides a systematic way to determine the degree of closeness between nodes without explicitly calculating the distance between each node. Different types of regular decomposition trees are introduced. The decomposition tree also represents a hierarchical clustering of the nodes, which improves in a graph theoretic sense as the graph drawing approaches a lower energy state. Finally, the decomposition tree provides a mechanism to view the hierarchical clustering on various levels of abstraction. Larger graphs can be represented more concisely, on a higher level of abstraction, with fewer graphics on screen.


Decomposition Tree Large Graph Space Decomposition Tree Code Graph Drawing 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Aaron Quigley
    • 1
  • Peter Eades
    • 1
  1. 1.Department of Computer Science and Software EngineeringUniv. of NewcastleCallaghanAustralia

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