Advertisement

Difference Map Readability for Dynamic Graphs

  • Daniel Archambault
  • Helen C. Purchase
  • Bruno Pinaud
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6502)

Abstract

Difference maps are one way to show changes between timeslices in a dynamic graph. They highlight, using colour, the nodes and edges that were added, removed, or persisted between every pair of adjacent timeslices. Although some work has used difference maps for visualization, no user study has been performed to gauge their performance. In this paper, we present a user study to evaluate the effectiveness of difference maps in comparison with presenting the evolution of the dynamic graph over time on three interfaces. We found evidence that difference maps produced significantly fewer errors when determining the number of edges inserted or removed from a graph as it evolves over time. Also, difference maps were significantly preferred on all tasks.

Keywords

User Study Practice Block Dynamic Graph Response Time Data Black Node 
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

  1. 1.
    Archambault, D., Purchase, H.C., Pinaud, B.: Animation, small multiples, and the effect of mental map preservation in dynamic graphs. IEEE Trans. on Visualization and Computer Graphics (to appear, 2010)Google Scholar
  2. 2.
    Archambault, D., Purchase, H.C., Pinaud, B.: The readability of path-preserving clusterings of graphs. Computer Graphics Forum (Proc. EuroVis) 29(3), 1173–1182 (2010)CrossRefGoogle Scholar
  3. 3.
    Archambault, D.: Structural differences between two graphs through hierarchies. In: Proc. of Graphics Interface, pp. 87–94 (2009)Google Scholar
  4. 4.
    Auber, D.: Tulip: A huge graph visualization framework. In: Mutzel, P., Jünger, M. (eds.) Graph Drawing Software. Mathematics and Visualization, pp. 105–126. Springer, Heidelberg (2003)Google Scholar
  5. 5.
    Boitmanis, K., Brandes, U., Pich, C.: Visualizing internet evolution on the autonomous systems level. In: Hong, S.-H., Nishizeki, T., Quan, W. (eds.) GD 2007. LNCS, vol. 4875, pp. 365–376. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  6. 6.
    Bourqui, R., Jourdan, F.: Revealing subnetwork roles using contextual visualization: Comparison of metabolic networks. In: Proc. 12th Int. Conf. on Information Visualisation (IV 2008), pp. 638–643 (2008)Google Scholar
  7. 7.
    Brandes, U., Wagner, D.: A bayesian paradigm for dynamic graph layout. In: DiBattista, G. (ed.) GD 1997. LNCS, vol. 1353, pp. 236–247. Springer, Heidelberg (1997)CrossRefGoogle Scholar
  8. 8.
    Cohen, R.F., Battista, G.D., Tollis, I.G., Cohen, R.F., Tamassia, R., Tollis, I.G.: A framework for dynamic graph drawing. In: ACM Symp. on Computational Geometry, pp. 261–270 (1992)Google Scholar
  9. 9.
    Diehl, S., Görg, C.: Graphs, they are a changing – dynamic graph drawing for a sequence of graphs. In: Goodrich, M.T., Kobourov, S.G. (eds.) GD 2002. LNCS, vol. 2528, pp. 23–31. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  10. 10.
    van Duijn, M., Zeggelink, E., Huisman, M., Stokman, F., Wasseur, F.: Evolution of sociology freshmen into a frendship network. The Journal of Math. Sociology 27(2), 153–191 (2003), http://www.stats.ox.ac.uk/~snijders/siena/vdBunt_data.htm CrossRefGoogle Scholar
  11. 11.
    Erten, C., Harding, P.J., Kobourov, S., Wampler, K., Yee, G.V.: GraphAEL: Graph animations with evolving layouts. In: Liotta, G. (ed.) GD 2003. LNCS, vol. 2912, pp. 98–110. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  12. 12.
    Farrugia, M., Quigley, A.: Effective temporal graph layout: A comparative study of animation versus static display methods. Journal of Information Visualization (to appear, 2010)Google Scholar
  13. 13.
    Frishman, Y., Tal, A.: Online dynamic graph drawing. IEEE Trans. on Visualization and Computer Graphics 14(4), 727–740 (2008)CrossRefGoogle Scholar
  14. 14.
    Görg, C., Birke, P., Pohl, M., Diehl, S.: Dynamic graph drawing of sequences of orthogonal and hierarchical graphs. In: Pach, J. (ed.) GD 2004. LNCS, vol. 3383, pp. 228–238. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  15. 15.
    Griffen, A.L., MacEachren, A.M., Hardisty, F., Steiner, E., Li, B.: A comparison of animated maps with static small-multiple maps for visually identifying space-time clusters. Annals of the Association of American Geographers 96(4), 740–753 (2006)CrossRefGoogle Scholar
  16. 16.
    Moen, S.: Drawing dynamic trees. IEEE Software 7(4), 21–28 (1990)CrossRefGoogle Scholar
  17. 17.
    Purchase, H.C., Hoggan, E., Görg, C.: How important is the “mental map”? – an empirical investigation of a dynamic graph layout algorithm. In: Kaufmann, M., Wagner, D. (eds.) GD 2006. LNCS, vol. 4372, pp. 184–195. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  18. 18.
    Purchase, H.C., Samra, A.: Extremes are better: Investigating mental map preservation in dynamic graphs. In: Stapleton, G., Howse, J., Lee, J. (eds.) Diagrams 2008. LNCS (LNAI), vol. 5223, pp. 60–73. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  19. 19.
    Robertson, G., Fernandez, R., Fisher, D., Lee, B., Stasko, J.: Effectiveness of animation in trend visualization. IEEE Trans. on Visualization and Computer Graphics (Proc. Vis/InfoVis 2008) 14(6), 1325–1332 (2008)CrossRefGoogle Scholar
  20. 20.
    Saffrey, P., Purchase, H.C.: The ”mental map” versus ”static aesthetic” compromise in dynamic graphs: A user study. In: Proc. of the 9th Australasian User Interface Conference, pp. 85–93 (2008)Google Scholar
  21. 21.
    Tufte, E.: Envisioning Information. Graphics Press (1990)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Daniel Archambault
    • 1
  • Helen C. Purchase
    • 2
  • Bruno Pinaud
    • 3
  1. 1.Clique Strategic Research ClusterUniversity College DublinIreland
  2. 2.Department of Computing ScienceUniversity of GlasgowUK
  3. 3.LaBRI UMR CNRS 5800 & INRIA Bordeaux Sud-OuestUniversité de Bordeaux IFrance

Personalised recommendations