Image-Based Graph Visualization: Advances and Challenges

  • Alexandru TeleaEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11282)


Visualizing large, multiply-attributed, and time-dependent graphs is one of the grand challenges of information visualization. In recent years, image-based techniques have emerged as a strong competitor in the arena of solutions for this task. While many papers on this topic have been published, the precise advantages and limitations of such techniques, and also how they relate to similar techniques in the more traditional fields of scientific visualization (scivis) and image processing, have not been sufficiently outlined. In this paper, we aim to provide such an overview and comparison. We highlight the main advantages of image-based graph visualization and propose a simple taxonomy for such techniques. Next, we highlight the differences between graph and scivis/image datasets that lead to limitations of current image-based graph visualization techniques. Finally, we consider these limitations to propose a number of future work directions for extending the effectiveness and range of image-based graph visualization.


Large graph visualization Image-based information visualization Multiscale visualization 


  1. 1.
    Abello, J., van Ham, F.: Matrix zoom: a visual interface to semi-external graphs. In: Ward, M., Munzner, T. (eds.) Proceedings of IEEE InfoVis, pp. 127–135 (2005)Google Scholar
  2. 2.
    Archambault, D., et al.: Temporal multivariate networks. In: Kerren, A., Purchase, H.C., Ward, M.O. (eds.) Multivariate Network Visualization. LNCS, vol. 8380, pp. 151–174. Springer, Cham (2014). Scholar
  3. 3.
    Borgo, R., et al.: Glyph-based visualization: foundations, design guidelines, techniques and applications. In: Sbert, M., Szirmay-Kalos, L. (eds.) Eurographics - State of the Art Reports. The Eurographics Association (2013)Google Scholar
  4. 4.
    Brehmer, M., Munzner, T.: A multi-level typology of abstract visualization tasks. IEEE TVCG 19(12), 2376–2385 (2013)Google Scholar
  5. 5.
    Byelas, H., Telea, A.: Visualizing multivariate attributes on software diagrams. In: Proceedings of IEEE CSMR, pp. 335–338 (2009)Google Scholar
  6. 6.
    Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE TPAMI 24(5), 603–619 (2002)CrossRefGoogle Scholar
  7. 7.
    Cui, W., Zhou, H., Qu, H., Wong, P.C., Li, X.: Geometry-based edge clustering for graph visualization. IEEE TVCG 14(6), 1277–1284 (2008)Google Scholar
  8. 8.
    Ellis, G., Dix, A.: A taxonomy of clutter reduction for information visualisation. IEEE TVCG 13(6), 1216–1223 (2007)Google Scholar
  9. 9.
    Ersoy, O., Hurter, C., Paulovich, F., Cantareiro, G., Telea, A.: Skeleton-based edge bundles for graph visualization. IEEE TVCG 17(2), 2364–2373 (2011)Google Scholar
  10. 10.
    Fabbri, R., da F. Costa, L., Torelli, J., Bruno, O.: 2D Euclidean distance transform algorithms: a comparative survey. ACM Comput. Surv. 40(1), 1–44 (2008)CrossRefGoogle Scholar
  11. 11.
    Gansner, E.R., Koren, Y.: Improved circular layouts. In: Kaufmann, M., Wagner, D. (eds.) GD 2006. LNCS, vol. 4372, pp. 386–398. Springer, Heidelberg (2007). Scholar
  12. 12.
    Garcke, H., Preusser, T., Rumpf, M., Telea, A., Weikard, U., van Wijk, J.J.: A continuous clustering method for vector fields. In: Moorhead, R. (ed.) Proceedings of IEEE Visualization, pp. 351–358 (2000)Google Scholar
  13. 13.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Pearson, London (2011)Google Scholar
  14. 14.
    Griebel, M., Preusser, T., Rumpf, M., Schweitzer, M.A., Telea, A.: Flow field clustering via algebraic multigrid. In: Proceedings of IEEE Visualization, pp. 35–42 (2004)Google Scholar
  15. 15.
    Herman, I., Melancon, G., Marshall, M.S.: Graph visualization and navigation in information visualization: a survey. IEEE TVCG 6(1), 24–43 (2000)Google Scholar
  16. 16.
    Holten, D.: Hierarchical edge bundles: visualization of adjacency relations in hierarchical data. IEEE TVCG 12(5), 741–748 (2006)Google Scholar
  17. 17.
    Holten, D., Isenberg, P., Van Wijk, J.J., Fekete, J.D.: An extended evaluation of the readability of tapered, animated, and textured directed-edge representations in node-link graphs. In: Battista, G.D., Fekete, J.D., Qu, H. (eds.) Proceedings of IEEE PacificVis, pp. 195–202 (2011)Google Scholar
  18. 18.
    Holten, D., Van Wijk, J.J.: Force-directed edge bundling for graph visualization. Comput. Graph. Forum 28(3), 983–990 (2009)CrossRefGoogle Scholar
  19. 19.
    Hurter, C., Ersoy, O., Fabrikant, S.I., Klein, T.R., Telea, A.C.: Bundled visualization of dynamic graph and trail data. IEEE TVCG 20(8), 1141–1157 (2014)Google Scholar
  20. 20.
    Hurter, C.: Image-Based Visualization: Interactive Multidimensional Data Exploration. Morgan & Claypool Publishers, San Rafael (2015)Google Scholar
  21. 21.
    Hurter, C., Ersoy, O., Telea, A.: Graph bundling by kernel density estimation. Comput. Graph. Forum 31(3), 865–874 (2012)CrossRefGoogle Scholar
  22. 22.
    Kruiger, J.F., Rauber, P.E., Martins, R.M., Kerren, A., Kobourov, S., Telea, A.C.: Graph layouts by t-SNE. Comput. Graph. Forum 36(3), 283–294 (2017)CrossRefGoogle Scholar
  23. 23.
    Landesberger, T.V., et al.: Visual analysis of large graphs: state-of-the-art and future research challenges. Comput. Graph. Forum 30(6), 1719–1749 (2011)CrossRefGoogle Scholar
  24. 24.
    Lee, B., Plaisant, C., Parr, C.S., Fekete, J.D., Henry, N.: Task taxonomy for graph visualization. In: Bertini, E., Plaisant, C., Santucci, G. (eds.) Proceedings of AVI BELIV, pp. 1–5. ACM (2006)Google Scholar
  25. 25.
    Lhuillier, A., Hurter, C., Telea, A.: FFTEB: edge bundling of huge graphs by the Fast Fourier Transform. In: Seo, J., Lee, B. (eds.) Proceedings of IEEE PacificVis (2017)Google Scholar
  26. 26.
    Lhuillier, A., Hurter, C., Telea, A.: State of the art in edge and trail bundling techniques. Comput. Graph. Forum 36(3), 619–645 (2017)CrossRefGoogle Scholar
  27. 27.
    van Liere, R., de Leeuw, W.: GraphSplatting: visualizing graphs as continuous fields. IEEE TVCG 9(2), 206–212 (2003)Google Scholar
  28. 28.
    Luebke, D.P.: A developer’s survey of polygonal simplification algorithms. IEEE CG&A 21(3), 24–35 (2001)Google Scholar
  29. 29.
    van der Maaten, L., Postma, E.: Dimensionality reduction: a comparative review. Technicla report TiCC TR 2009-005, Tilburg University, Netherlands (2009).
  30. 30.
    Marcus, G.: Deep learning: a critical appraisal (2018). arXiv:1801.00631[cs.AI]
  31. 31.
    Martins, R., Coimbra, D., Minghim, R., Telea, A.: Visual analysis of dimensionality reduction quality for parameterized projections. Comput. Graph. 41, 26–42 (2014)CrossRefGoogle Scholar
  32. 32.
    Martins, R.M., Kruiger, J.F., Minghim, R., Telea, A.C., Kerren, A.: MVN-Reduce: dimensionality reduction for the visual analysis of multivariate networks. In: Kozlikova, B., Schreck, T., Wischgoll, T. (eds.) Proceedings of Eurographics - Short Papers (2017)Google Scholar
  33. 33.
    Minard, C.J.: Carte figurative et approximative des quantités de vin français exportés par mer en 1864 (1865)Google Scholar
  34. 34.
    Munzner, T.: Visualization Analysis and Design. CRC Press, Boca Raton (2014)CrossRefGoogle Scholar
  35. 35.
    Newbery, F.: Edge concentration: a method for clustering directed graphs. ACM SIGSOFT Softw. Eng. Notes 14(7), 76–85 (1989)CrossRefGoogle Scholar
  36. 36.
    Nguyen, Q., Eades, P., Hong, S.-H.: StreamEB: stream edge bundling. In: Didimo, W., Patrignani, M. (eds.) GD 2012. LNCS, vol. 7704, pp. 400–413. Springer, Heidelberg (2013). Scholar
  37. 37.
    Nguyen, Q., Eades, P., Hong, S.H.: On the faithfulness of graph visualizations. In: Carpendale, S., Chen, W., Hong, S. (eds.) Proceedings of IEEE PacificVis (2013)Google Scholar
  38. 38.
    Nocaj, A., Ortmann, M., Brandes, U.: Untangling hairballs. In: Duncan, C., Symvonis, A. (eds.) GD 2014. LNCS, vol. 8871, pp. 101–112. Springer, Heidelberg (2014). Scholar
  39. 39.
    Oztireli, A.C., Gross, M.: Perceptually based downscaling of images. ACM TOG 34(4), 77 (2015)CrossRefGoogle Scholar
  40. 40.
    Pal, N., Pal, S.K.: A review on image segmentation techniques. Pattern Recogn. 26(9), 1277–1294 (1993)CrossRefGoogle Scholar
  41. 41.
    Peysakhovich, V., Hurter, C., Telea, A.: Attribute-driven edge bundling for general graphs with applications in trail analysis. In: Liu, S., Scheuermann, G., Takahashi, S. (eds.) Proceedings of IEEE PacificVis, pp. 39–46 (2015)Google Scholar
  42. 42.
    Phan, D., Xiao, L., Yeh, R., Hanrahan, P., Winograd, T.: Flow map layout. In: Stasko, J., Ward, M. (eds.) Proceedings of InfoVis, pp. 219–224 (2005)Google Scholar
  43. 43.
    Post, F.H., Vrolijk, B., Hauser, H., Laramee, R., Doleisch, H.: The state of the art in flow visualisation: feature extraction and tracking. Comput. Graph. Forum 22(4), 775–792 (2003)CrossRefGoogle Scholar
  44. 44.
    Ropinski, T., Oeltze, S., Preim, B.: Survey of glyph-based visualization techniques for spatial multivariate medical data. Comput. Graph. 35(2), 392–401 (2011)CrossRefGoogle Scholar
  45. 45.
    Schaeffer, S.: Graph clustering. Comput. Sci. Rev. 1(1), 27–64 (2007)CrossRefGoogle Scholar
  46. 46.
    Schroeder, W., Zarge, J., Lorensen, W.: Decimation of triangle meshes. In: Thomas, J.J. (ed.) Proceedings of ACM SIGGRAPH, pp. 65–70 (1992)CrossRefGoogle Scholar
  47. 47.
    Schulz, H.J., Hurter, C.: Grooming the hairball-how to tidy up network visualizations? In: Proceedings of IEEE InfoVis (Tutorials) (2013)Google Scholar
  48. 48.
    Siddiqi, K., Pizer, S.: Medial Representations: Mathematics, Algorithms and Applications. Springer, Dordrecht (2009). Scholar
  49. 49.
    Silverman, B.: Density Estimation for Statistics and Data Analysis. Monographs on Statistics and Applied Probability, vol. 26 (1992)Google Scholar
  50. 50.
    Sorzano, C., Vargas, J., Pascual-Montano, A.: A survey of dimensionality reduction techniques (2014).
  51. 51.
    Tamassia, R.: Handbook of Graph Drawing and Visualization. CRC Press, Boca Raton (2013)zbMATHGoogle Scholar
  52. 52.
    Telea, A.: Data Visualization: Principles and Practice, 2nd edn. CRC Press, Boca Raton (2015)Google Scholar
  53. 53.
    Telea, A., Ersoy, O.: Image-based edge bundles: simplified visualization of large graphs. Comput. Graph. Forum 29(3), 543–551 (2010)CrossRefGoogle Scholar
  54. 54.
    Telea, A., Maccari, A., Riva, C.: An open toolkit for prototyping reverse engineering visualizations. In: Ebert, D., Brunet, P., Navazzo, I. (eds.) Proceedings of Data Visualization (IEEE VisSym), pp. 67–75 (2002)Google Scholar
  55. 55.
    Tollis, I., Battista, G.D., Eades, P., Tamassia, R.: Graph Drawing: Algorithms for the Visualization of Graphs. Prentice Hall, Upper Saddle River (1999)zbMATHGoogle Scholar
  56. 56.
    Trümper, J., Döllner, J., Telea, A.: Multiscale visual comparison of execution traces. In: Kagdi, H., Poshyvanyk, D., Penta, M.D. (eds.) Proceedings of IEEE ICPC (2013)Google Scholar
  57. 57.
    Tufte, E.R.: The Visual Display of Quantitative Information. Graphics Press, Cheshire (1992)Google Scholar
  58. 58.
    Van Wijk, J.J., van de Wetering, H.: Cushion treemaps: visualization of hierarchical information. In: Wills, G., Keim, D. (eds.) Proceedings of IEEE InfoVis, pp. 73–82 (1999)Google Scholar
  59. 59.
    Weickert, J., Hagen, H.: Visualization and Processing of Tensor Fields. Springer, Heidelberg (2007). Scholar
  60. 60.
    van Wijk, J.J.: Image based flow visualization. Proc. ACM TOG (SIGGRAPH) 21(3), 745–754 (2002)Google Scholar
  61. 61.
    Zhou, H., Xu, P., Yuan, X., Qu, H.: Edge bundling in information visualization. Tsinghua Sci. Technol. 18(2), 145–156 (2013)CrossRefGoogle Scholar
  62. 62.
    van der Zwan, M., Codreanu, V., Telea, A.: CUBu: universal real-time bundling for large graphs. IEEE TVCG 22(12), 2550–2563 (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Bernoulli InstituteUniversity of GroningenGroningenThe Netherlands

Personalised recommendations