An Incremental Layout Method for Visualizing Online Dynamic Graphs

  • Tarik CrnovrsaninEmail author
  • Jacqueline Chu
  • Kwan-Liu Ma
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9411)


Graphs provide a visual means for examining relation data and force-directed methods are often used to lay out graphs for viewing. Making sense of a dynamic graph as it evolves over time is challenging, and previous force-directed methods were designed for static graphs. In this paper, we present an incremental version of a multilevel multi-pole layout method with a refinement scheme incorporated, which enables us to visualize online dynamic networks while maintaining a mental map of the graph structure. We demonstrate the effectiveness of our method and compare it to previous methods using several network data sets.


Dynamic graphs Streaming data Graph layout 



This research is sponsored in part by the U.S. National Science Foundation via grants NSF DRL-1323214 and NSF IIS-1320229, the U.S. Department of Energy through grant DE-FC02-12ER26072, and also the UC Davis RISE program.


  1. 1.
    Archambault, D., Purchase, H.C., Pinaud, B.: Animation, small multiples, and the effect of mental map preservation in dynamic graphs. IEEE Trans. Vis. Comput. Graph. 17(4), 539–552 (2011)CrossRefGoogle Scholar
  2. 2.
    Bach, B., Pietriga, E., Fekete, J.D.: GraphDiaries: animated transitions and temporal navigation for dynamic networks. IEEE Trans. Vis. Comput. Graph. 20(5), 740–754 (2014)CrossRefGoogle Scholar
  3. 3.
    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
  4. 4.
    Brandes, U., Fleischer, D., Puppe, T.: Dynamic spectral layout of small worlds. In: Healy, P., Nikolov, N.S. (eds.) GD 2005. LNCS, vol. 3843, pp. 25–36. Springer, Heidelberg (2006) CrossRefGoogle Scholar
  5. 5.
    Brandes, U., Mader, M.: A quantitative comparison of stress-minimization approaches for offline dynamic graph drawing. In: Speckmann, B. (ed.) GD 2011. LNCS, vol. 7034, pp. 99–110. Springer, Heidelberg (2011) CrossRefGoogle Scholar
  6. 6.
    Brandes, U., Wagner, D.: A bayesian paradigm for dynamic graph layout. In: Di Battista, G. (ed.) GD 1997. LNCS, vol. 1353, pp. 236–247. Springer, Heidelberg (1997) CrossRefGoogle Scholar
  7. 7.
    Che, L., Liang, J., Yuan, X., Shen, J., Xu, J., Li, Y.: Laplacian-based dynamic graph visualization. In: Visualization Symposium (PacificVis), 2015 IEEE Pacific, pp. 69–73 (2015)Google Scholar
  8. 8.
    Diehl, S., Görg, C.: Graphs, they are changing. In: Goodrich, M.T., Kobourov, S.G. (eds.) GD 2002. LNCS, vol. 2528, pp. 23–31. Springer, Heidelberg (2002) CrossRefGoogle Scholar
  9. 9.
    Erten, C., Harding, P.J., Kobourov, S.G., Wampler, K., Yee, G.: Graphael: graph animations with evolving layouts. In: Liotta, G. (ed.) GD 2003. LNCS, vol. 2912, pp. 98–110. Springer, Heidelberg (2004) CrossRefGoogle Scholar
  10. 10.
    Frishman, Y., Tal, A.: Online dynamic graph drawing. IEEE Trans. Vis. Comput. Graph. 14(4), 727–740 (2008)CrossRefGoogle Scholar
  11. 11.
    Godiyal, A., Hoberock, J., Garland, M., Hart, J.C.: Rapid multipole graph drawing on the GPU. In: Tollis, I.G., Patrignani, M. (eds.) GD 2008. LNCS, vol. 5417, pp. 90–101. Springer, Heidelberg (2009) CrossRefGoogle Scholar
  12. 12.
    Gorochowski, T., di Bernardo, M., Grierson, C.: Using aging to visually uncover evolutionary processes on networks. IEEE Trans. Vis. Comput. Graph. 18(8), 1343–1352 (2012)CrossRefGoogle Scholar
  13. 13.
    Hachul, S., Jünger, M.: An experimental comparison of fast algorithms for drawing general large graphs. In: Healy, P., Nikolov, N.S. (eds.) GD 2005. LNCS, vol. 3843, pp. 235–250. Springer, Heidelberg (2006) CrossRefGoogle Scholar
  14. 14.
    Harel, D., Koren, Y.: A fast multi-scale method for drawing large graphs. In: Marks, J. (ed.) GD 2000. LNCS, vol. 1984, pp. 183–196. Springer, Heidelberg (2001) CrossRefGoogle Scholar
  15. 15.
    Hu, Y., Kobourov, S.G., Veeramoni, S.: Embedding, clustering and coloring for dynamic maps. In: Visualization Symposium (PacificVis), 2012 IEEE Pacific, pp. 33–40 (2012)Google Scholar
  16. 16.
    Khoury, M., Hu, Y., Krishnan, S., Scheidegger, C.: Drawing large graphs by low-rank stress majorization. Comput. Graph. Forum 31(3pt1), 975–984 (2012)CrossRefGoogle Scholar
  17. 17.
    Koren, Y., Carmel, L., Harel, D.: Drawing huge graphs by algebraic multigrid optimization. Multiscale Model. Simul. 1, 645–673 (2003)MathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    Kumar, G., Garland, M.: Visual exploration of complex time-varying graphs. IEEE Trans. Vis. Comput. Graph. 12(5), 805–812 (2006)CrossRefGoogle Scholar
  19. 19.
    Lee, Y.Y., Lin, C.C., Yen, H.C.: Mental map preserving graph drawing using simulated annealing. In: Proceedings of the 2006 Asia-Pacific Symposium on Information Visualisation, APVis 2006, Vol. 60, pp. 179–188 (2006)Google Scholar
  20. 20.
    Mcfarland, D.: Student resistance: how the formal and informal organization of classrooms facilitate everyday forms of student defiance. Am. J. Sociol. 107(3), 612–678 (2001)CrossRefGoogle Scholar
  21. 21.
    Misue, K., Eades, P., Lai, W., Sugiyama, K.: Layout adjustment and the mental map. J. Vi. Lang. Comput. 6(2), 183–210 (1995)CrossRefGoogle Scholar
  22. 22.
    North, S.C.: Incremental layout in dynadag. In: Brandenburg, F.J. (ed.) GD 1995. LNCS, vol. 1027, pp. 409–418. Springer, Heidelberg (1996) CrossRefGoogle Scholar
  23. 23.
    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
  24. 24.
    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
  25. 25.
    Tufte, E.R.: Envisioning Information. Graphic Press, Cheshire (1990)Google Scholar
  26. 26.
    Yao, Y.: Collection and streaming of graph datasets.

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Tarik Crnovrsanin
    • 1
    Email author
  • Jacqueline Chu
    • 1
  • Kwan-Liu Ma
    • 1
  1. 1.University of CaliforniaDavisUSA

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