A Bayesian Paradigm for Dynamic Graph Layout

Brandes, Ulrik and Wagner, Dorothea (1998) A Bayesian Paradigm for Dynamic Graph Layout. In: Graph Drawing 5th International Symposium, GD '97, September 18-20, 1997, Rome, Italy , pp. 236-247 (Official URL: http://dx.doi.org/10.1007/3-540-63938-1_66).

Full text not available from this repository.

Abstract

Dynamic graph layout refers to the layout of graphs that change over time. These changes are due to user interaction, algorithms, or other underlying processes determining the graph. Typically, users spend a noteworthy amount of time to get familiar with a layout, i.e. they build a mental map [ELMS91]. To retain this map at least partially, consecutive layouts of similar graphs should not differ significantly. Still, each of these layouts should adhere to constraints and criteria that have been specified to improve meaning and readability of a drawing. In [BW97], we introduced random field models for graph layout. As a major advantage of this formulation, many different layout models can be represented uniformly by random variables. This uniformity enables us to now present a framework for dynamic layout of arbitrary random field models. Our approach is based on Bayesian decision theory and formalizes common sense procedures. Example applications of our framework are dynamic versions of two well-known layout models: Eades' spring embedder [Ead84], and Tamssia's bend-minimum orthogonal layout model for plane graphs [Tam87].

Item Type:Conference Paper
Additional Information:10.1007/3-540-63938-1_66
Classifications:M Methods > M.300 Dynamic / Incremental / Online
G Algorithms and Complexity > G.210 Bends
P Styles > P.600 Poly-line > P.600.700 Orthogonal
M Methods > M.400 Force-directed / Energy-based
ID Code:83

Repository Staff Only: item control page

References

Karl-Friedrich Böhringer and Frances Newberry Paulisch. Using constraints to achieve stability in automatic graph layout algorithms. In CHI'90 Proceedings, pages 43-51. ACM, The Association for Computing Machinery, New York, 1990.

Ulrik Brandes and Dorothea Wagner. Random field models for graph layout. Konstanzer Schriften in Mathematik und Informatik 33, Universität Konstanz, 1997.

Robert F. Cohen, Giuseppe Di Battista, Roberto Tamassia, Ioannis G. Tollis, and P. Bertolazzi. A Framework for Dynamic Graph Drawing. In Proc. of 8th Annual Computational Geometry, 6/92, Berlin, Germany, pages 261-270. ACM, The Association for Computing Machinery, New York, 1992.

Robert F. Cohen, Giuseppe Di Battista, Roberto Tamassia and Ioannis G. Tollis. Dynamic graph drawings: Trees, series-parallel digraphs, and planar st-digraphs. SIAM J. Coput., 24(5):970-1001, 1995.

R. Chellappa and A.K. Jain. Markov Random Fields: Theory and Applications. Academic Press, 1993.

Ron Davidson and David Harel. Drawing graphs nicely using simulated annealing. ACM Transactions on Graphics, 15(4):301-331, 1996.

Peter Eades. A heuristic for graph drawing. Congressus Numerantium, 42:149-160, 1984.

Peter Eades, W. Lai, Kazuo Misue, and Kozo Sugiyama. Preserving the mental map of diagram. Proceedings of Compugraphics, 9:24-33, 1991.

Stuart German and Donald German. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transations on Pattern Analysis and Machine Intelligence, 6(6):721-741, 1984.

Stuart German and Donald E. McClure. Bayesian image analysis: an application to single photon emission tomography. Proc. American Statistical Association, Statistical Computing Section, pages 12-18, 1985.

Xavier Guyon. Random Fields on a Network. Springer, 1994.

Kelly A. Lyons. Cluster busting in anchored graph drawing. In Proceedings of the '92 CAS Conference/CASCON'92, Toronto, 1992, pages 7-17, 1992.

Stephen Nort. Incremental Layout in DynaDAG. Proceedings of GD'95, pages 409-418. Springer-Verlag, Lecture Notes in Computer Science, vol. 1027, 1996.

Achilleas Papakostas and Ioannis G. Tollis. Issues in Interactive Orthogonal Graph Drawing. Proceedings of GD'95, pages 419-430. Springer-Verlag, Lecture Notes in Computer Science, vol. 1027, 1996.

Roberto Tamassia. On embedding a graph in the grid with the minmum number of bends. SIAM J. Comput., 16(3): 421-444, 1987.

Gerhard Winkler. Image Analysis, Random Fields and Dynamic Monte Carlo Methods, volume 27 of Applications of Mathematics. Springer, 1995.