Abstract
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.
Carsten Friedrich’s research partially supported by the Australian Defence Science and Technology Organization (DSTO).
Michael Houle is on leave from the Basser Department of Computer Science, University of Sydney.
Chapter PDF
Similar content being viewed by others
Keywords
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.
References
M. S. Aldenderfer and R. K. Blashfield. Cluster Analysis. Sage Publications, Beverly Hills, USA, 1984.
Giuseppe Di Battista, Peter Eades, Roberto Tamassia, and Ioannis G. Tollis. Graph drawing: algorithms for the visualization of graphs. Prentice-Hall Inc., 1999.
F. Bertault. A force-directed algorithm that preserves edge-crossing properties. Information Processing Letters, 74(1-2):7–13, 2000.
Mark de Berg, Marc van Kreveld, and Mark Overmars. Computational Geometry: Algorithms and Applications, chapter 9, pages 188–200. Springer Verlag, 2nd edition, 1998.
C. Friedrich. The ffGraph library. Technical Report 9520, Universität Passau, Dezember 1995.
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.
Carsten Friedrich and Peter Eades. Graph drawing in motion. Submitted to Journal of Graph Algorithms and Applications, 2001.
R. P. Haining. Spatial Data Analysis in the Social and Enviromental Sciences. Cambridge University Press, UK, 1990.
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.
L. Kaufman and P. J. Rousseeuw. Finding Groups in Data: An Introduction to Cluster Analysis. John Wiley & Sons, NY, USA, 1990.
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.
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.
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.
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.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Friedrich, C., Houle, M.E. (2002). Graph Drawing in Motion II. In: Mutzel, P., Jünger, M., Leipert, S. (eds) Graph Drawing. GD 2001. Lecture Notes in Computer Science, vol 2265. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45848-4_18
Download citation
DOI: https://doi.org/10.1007/3-540-45848-4_18
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-43309-5
Online ISBN: 978-3-540-45848-7
eBook Packages: Springer Book Archive