An Experimental Study on Distance-Based Graph Drawing

Brandes, Ulrik and Pich, Christian (2009) An Experimental Study on Distance-Based Graph Drawing. In: Graph Drawing 16th International Symposium, GD 2008, September 21- 24, 2008, Heraklion, Crete, Greece , pp. 218-229 (Official URL:

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In numerous application areas, general undirected graphs need to be drawn, and force-directed layout appears to be the most frequent choice. We present an extensive experimental study showing that, if the goal is to represent the distances in a graph well, a combination of two simple algorithms based on variants of multidimensional scaling is to be preferred because of their efficiency, reliability, and even simplicity. We also hope that details in the design of our study help advance experimental methodology in algorithm engineering and graph drawing, independent of the case at hand.

Item Type:Conference Paper
Additional Information:10.1007/978-3-642-00219-9_21
Classifications:G Algorithms and Complexity > G.070 Area / Edge Length
M Methods > M.400 Force-directed / Energy-based
P Styles > P.720 Straight-line
ID Code:908

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Borg, I., Groenen, P.J.F.: Modern Multidimensional Scaling. Springer, 2 edn., Heidelberg (2005)

Brandenburg, F.J., Himsolt, M., Rohrer, C.: An experimental comparison of forcedirected and randomized graph drawing algorithms. In: GD 1995. LNCS, vol. 1027, pp. 76–87, Springer, Heidelberg (1996)

Brandes, U.: Drawing on physical analogies. In: Kaufmann, M., Wagner, D. (eds.) Drawing Graphs: Methods and Models, LNCS, vol. 2025, pp. 71–86. Springer, Heidelberg (2001)

Brandes, U., Pich, C.: Eigensolver methods for progressive multidimensional scaling of large data. In: GD 2006. LNCS, vol. 4372, pp. 42–53 (2007)

Buja, A., Swayne, D.F.: Visualization methodology for multidimensional scaling. Journal of Classification 19, 7–43 (2002)

Civril, A., Magdon-Ismail, M., Bocek-Rivele, E.: SSDE: Fast graph drawing using sampled spectral distance embedding. In: GD 2006. LNCS, vol. 4372, pp. 30–41, Springer, Heidelberg (2007)

Cohen, J.D.: Drawing graphs to convey proximity. ACM Transactions on Computer-Human Interaction 4(3), 197–229 (1997)

Cox, T.F., Cox, M.A.A.: Multidimensional Scaling, 2nd edn.CRC/Chapman and Hall, 2 edn. (2001)

Eades, P., Wormald, N.C.: Fixed edge-length graph drawing is NP-hard. Discrete Applied Mathematics 28(2), 111–134 (1990)

Freeman, L.C.: Graph layout techniques and multidimensional analysis. Journal of Social Structure 1 (2000)

Gajer, P., Kobourov, S.: GRIP – Graph drawing with intelligent placement. In: GD 2000. LNCS, vol. 1984, pp. 222–228. Springer, Heidelberg (2001)

Gansner, E.R., Koren, Y., North, S.C.: Graph drawing by stress ma jorization. In: GD 2004. LNCS, vol. 3383, pp. 239–250. Springer, Heidelberg (2005)

Hachul, S., Jünger, M.: An experimental comparison of fast algorithms for drawing general large graphs. In: GD 2005. LNCS, vol. 3843, pp. 235–250. Springer, Heidelberg (2006)

Harel, D., Koren, Y.: Graph drawing by high-dimensional embedding. In: GD 2002. LNCS, vol. 2528, pp. 207–219, Springer, Heidelberg (2002)

Kamada, T., Kawai, S.: An algorithm for drawing general undirected graphs. Information Processing Letters 31, 7–15 (1989)

Kruskal, J.B., Seery, J.B.: Designing network diagrams. In: Proc. First General Conference on Social Graphics. pp. 22–50 (1980)

Kruskal, J.B.: Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika 29(1), 1–27 (1964)

de Leeuw, J.: Applications of convex analysis to multidimensional scaling. In: Barra, J.R., Brodeau, F., Romier, G., van Cutsem, B. (eds.) Recent Developments in Statistics, pp. 133–145. Amsterdam: North-Holland (1977)

McGee, V.E.: The multidimensional scaling of “elastic” distances. Br. J. Math. Stat. Psychol. 19, 181–196 (1966)

Sammon, J.W.: A nonlinear mapping for data structure analysis. IEEE Transactions on Computers 18(5), 401–409 (1969)

Sibson, R.: Studies in the robustness of multidimensional scaling: Procrustes statistics. J. R. Stat. Sooc. 40(2), 234–238 (1978)

de Silva, V., Tenenbaum, J.B.: Sparse multidimensional scaling using landmark points. Tech. rep., Stanford University (2004)