Towards Partially Automatic Search of Edge Bundling Parameters

  • Evgheni PolisciucEmail author
  • Filipe Assunção
  • Penousal Machado
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10783)


Edge bundling methods are used in flow maps and graphs to reduce the visual clutter, which is generated when representing complex and heterogeneous data. Nowadays, there are many edge bundling algorithms that have been successfully applied to a wide range of problems in graph representation. However, the majority of these methods are still difficult to use and apply on real world problems by the experts from other areas. This is due to the complexity of the algorithms and concepts behind them, as well as a strong dependence on their parametrization. In addition, the majority of edge bundling methods need to be fine-tuned when applied on different datasets. This paper presents a new approach that helps finding near-optimal parameters for solving such issues in edge bundling algorithms, regardless of the configuration of the input graph. Our method is based on evolutionary computation, allowing the users to find edge bundling solutions for their needs. In order to understand the effectiveness of the evolutionary algorithm in such kind of tasks, we performed experiments with automatic fitness functions, as well as with partially user-guided evolution. We tested our approach in the optimization of the parameters of two different edge bundling algorithms. Results are compared using objective criteria and a critical discussion of the obtained graphical solutions.


Information visualization Edge bundling Graph representation Genetic algorithm 



This project has been supported by Fundação para a Ciência e Tecnologia (FCT), Portugal, under the grants SFRH/BD/109745/2015 and SFRH/BD/114865/2016.


  1. 1.
    Dent, B.: Dynamic representation: the design of flow maps. In: Cartography: Thematic Map Design, vol. 1. WCB/McGraw-Hill (1999)Google Scholar
  2. 2.
    Phan, D., Xiao, L., Yeh, R.B., Hanrahan, P., Winograd, T.: Flow map layout. In: IEEE Symposium on Information Visualization (InfoVis 2005), 23–25 October 2005, Minneapolis, MN, USA, p. 29 (2005)Google Scholar
  3. 3.
    Holten, D.: Hierarchical edge bundles: visualization of adjacency relations in hierarchical data. IEEE Trans. Vis. Comput. Graph. 12(5), 741–748 (2006)CrossRefGoogle Scholar
  4. 4.
    Holten, D., van Wijk, J.J.: Force-directed edge bundling for graph visualization. Comput. Graph. Forum 28(3), 983–990 (2009)CrossRefGoogle Scholar
  5. 5.
    Hurter, C., Ersoy, O., Telea, A.: Graph bundling by kernel density estimation. Comput. Graph. Forum 31(3), 865–874 (2012)CrossRefGoogle Scholar
  6. 6.
    Peysakhovich, V., Hurter, C., Telea, A.: Attribute-driven edge bundling for general graphs with applications in trail analysis. In: 2015 IEEE Pacific Visualization Symposium, PacificVis 2015, Hangzhou, China, 14–17 April 2015, pp. 39–46 (2015)Google Scholar
  7. 7.
    Polisciuc, E., Cruz, P., Amaro, H., Maçãs, C., Machado, P.: Flow map of products transported among warehouses and supermarkets. In: Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2016). IVAPP, Rome, Italy, 27–29 February 2016, vol. 2, pp. 179–188 (2016)Google Scholar
  8. 8.
    Romero, J., Machado, P., Carballal, A., Correia, J.: Computing aesthetics with image judgement systems. In: McCormack, J., d’Inverno, M. (eds.) Computers and Creativity, pp. 295–322. Springer, Heidelberg (2012). CrossRefGoogle Scholar
  9. 9.
    Takagi, H.: Interactive evolutionary computation: fusion of the capabilities of EC optimization and human evaluation. Proc. IEEE 89(9), 1275–1296 (2001)CrossRefGoogle Scholar
  10. 10.
    Machado, P., Romero, J., Cardoso, A., Santos, A.: Partially interactive evolutionary artists. New Gener. Comput. 23(2), 143–155 (2005)CrossRefGoogle Scholar
  11. 11.
    Machado, P., Cardoso, A.: NEvAr – the assessment of an evolutionary art tool. In: Wiggins, G. (ed.) AISB’00 Symposium on Creative and Cultural Aspects and Applications of AI and Cognitive Science, Birmingham, UK (2000)Google Scholar
  12. 12.
    Ralley, D.: Genetic algorithms as a tool for melodic development. In: International Computer Music Conference, pp. 501–502 (1995)Google Scholar
  13. 13.
    Rooke, S.: The evolutionary art of steven rooke (1996)Google Scholar
  14. 14.
    Sims, K.: Artificial evolution for computer graphics, vol. 25. ACM (1991)Google Scholar
  15. 15.
    Battista, G.D., Eades, P., Tamassia, R., Tollis, I.G.: Graph Drawing: Algorithms for the Visualization of Graphs. Prentice-Hall, Upper Saddle River (1999)zbMATHGoogle Scholar
  16. 16.
    Kuntz, P., Pinaud, B., Lehn, R.: Minimizing crossings in hierarchical digraphs with a hybridized genetic algorithm. J. Heuristics 12(1–2), 23–36 (2006)CrossRefzbMATHGoogle Scholar
  17. 17.
    Branke, J., Bucher, F., Schmeck, H.: Using genetic algorithms for drawing undirected graphs. In: The Third Nordic Workshop on Genetic Algorithms and their Applications. Citeseer (1996)Google Scholar
  18. 18.
    Eloranta, T., Mäkinen, E.: Timga: a genetic algorithm for drawing undirected graphs. Divulgaciones Matemáticas 9(2), 155–171 (2001)MathSciNetzbMATHGoogle Scholar
  19. 19.
    Wu, H.-Y., Takahashi, S., Lin, C.-C., Yen, H.-C.: A zone-based approach for placing annotation labels on metro maps. In: Dickmann, L., Volkmann, G., Malaka, R., Boll, S., Krüger, A., Olivier, P. (eds.) SG 2011. LNCS, vol. 6815, pp. 91–102. Springer, Heidelberg (2011). CrossRefGoogle Scholar
  20. 20.
    Tanahashi, Y., Ma, K.: Design considerations for optimizing storyline visualizations. IEEE Trans. Vis. Comput. Graph. 18(12), 2679–2688 (2012)CrossRefGoogle Scholar
  21. 21.
    House, D.H., Bair, A., Ware, C.: An approach to the perceptual optimization of complex visualizations. IEEE Trans. Vis. Comput. Graph. 12(4), 509–521 (2006)CrossRefGoogle Scholar
  22. 22.
    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). CrossRefGoogle Scholar
  23. 23.
    Pressley, A.: Elementary Differential Geometry. Springer, London (2010). CrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.CISUC, Department of Informatics EngineeringUniversity of CoimbraCoimbraPortugal

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