Graph Drawing in Motion II

  • Carsten Friedrich
  • Michael E. Houle
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2265)


Enabling the user of a graph drawing system to preserve the mental map between two different layouts of a graph is a major problem. Whenever a layout in a graph drawing system is modified, the mental map of the user must be preserved. One way in which the user can be helped in understanding a change of layout is through animation of the change. In this paper, we present clustering-based strategies for identifying groups of nodes sharing a common, simple motion from initial layout to final layout. Transformation of these groups is then handled separately in order to generate a smooth animation.


Delaunay Triangulation Initial Partition Graph Drawing Graph Layout Threshold Graph 
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.


  1. 1.
    M. S. Aldenderfer and R. K. Blashfield. Cluster Analysis. Sage Publications, Beverly Hills, USA, 1984.Google Scholar
  2. 2.
    Giuseppe Di Battista, Peter Eades, Roberto Tamassia, and Ioannis G. Tollis. Graph drawing: algorithms for the visualization of graphs. Prentice-Hall Inc., 1999.Google Scholar
  3. 3.
    F. Bertault. A force-directed algorithm that preserves edge-crossing properties. Information Processing Letters, 74(1-2):7–13, 2000.zbMATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    Mark de Berg, Marc van Kreveld, and Mark Overmars. Computational Geometry: Algorithms and Applications, chapter 9, pages 188–200. Springer Verlag, 2nd edition, 1998.Google Scholar
  5. 5.
    C. Friedrich. The ffGraph library. Technical Report 9520, Universität Passau, Dezember 1995.Google Scholar
  6. 6.
    Carsten Friedrich and Peter Eades. The marey graph animation tool demo. In Proc. of the 8th Internat. Symposium on Graph Drawing (GD’2000), pages 396–406, 2000.Google Scholar
  7. 7.
    Carsten Friedrich and Peter Eades. Graph drawing in motion. Submitted to Journal of Graph Algorithms and Applications, 2001.Google Scholar
  8. 8.
    R. P. Haining. Spatial Data Analysis in the Social and Enviromental Sciences. Cambridge University Press, UK, 1990.Google Scholar
  9. 10.
    Mao Lin Huang and Peter Eades. A fully animated interactive system for clustering and navigating huge graphs. In Sue H. Whitesides, editor, Proc. of the 6th Internat. Symposium on Graph Drawing (GD’98), pages 374–383, 1998.Google Scholar
  10. 11.
    L. Kaufman and P. J. Rousseeuw. Finding Groups in Data: An Introduction to Cluster Analysis. John Wiley & Sons, NY, USA, 1990.Google Scholar
  11. 12.
    J. MacQueen. Some methods for classification and analysis of multivariate observations. In L. Le Cam, and J. Neyman, editor, 5th Berkley Symposium on Mathematical Statistics and Probability, pages 281–297, 1967.Google Scholar
  12. 13.
    G. A. Miller. The magical number seven, plus or minus two: some limits on our capacity for processing information. The Psychological Review, pages 63:81–97, 1956.CrossRefGoogle Scholar
  13. 14.
    U. Fayyad P. S. Bradley and C. Reina. Scaling clustering algorithms to large databases. In R. Agrawal and P. Stolorz, editor, Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, pages 9–15, 1998.Google Scholar
  14. 15.
    C. Reina U. Fayyad and P. S. Bradley. Initialization of iterative refinement clustering algorithms. In R. Agrawal and P. Stolorz, editor, Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, pages 194–198, 1998.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Carsten Friedrich
    • 1
  • Michael E. Houle
    • 2
  1. 1.Basser Department of Computer ScienceThe University of SydneyAustralia
  2. 2.IBM ResearchTokyo Research LaboratoryJapan

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