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Visual Similarity Perception of Directed Acyclic Graphs: A Study on Influencing Factors

  • K. Ballweg
  • M. Pohl
  • G. Wallner
  • T. von Landesberger
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10692)

Abstract

While visual comparison of directed acyclic graphs (DAGs) is commonly encountered in various disciplines (e.g., finance, biology), knowledge about humans’ perception of graph similarity is currently quite limited. By graph similarity perception we mean how humans perceive commonalities and differences in graphs and herewith come to a similarity judgment. As a step toward filling this gap the study reported in this paper strives to identify factors which influence the similarity perception of DAGs. In particular, we conducted a card-sorting study employing a qualitative and quantitative analysis approach to identify (1) groups of DAGs that are perceived as similar by the participants and (2) the reasons behind their choice of groups. Our results suggest that similarity is mainly influenced by the number of levels, the number of nodes on a level, and the overall shape of the graph.

Keywords

Graphs Perception Similarity Comparison Visualization 

Notes

Acknowledgments

This work was financially supported by the Deutsche Forschungsgemeinschaft e.V. (DFG, LA 3001/2-1) and the Austrian Science Fund (FWF, I 2703-N31).

References

  1. 1.
    Archambault, D.: Structural differences between two graphs through hierarchies. In: Proceedings of Graphics, pp. 87–94. Canadian Information Processing Society (2009)Google Scholar
  2. 2.
    Archambault, D., Purchase, H.C., Pinaud, B.: Difference map readability for dynamic graphs. In: Brandes, U., Cornelsen, S. (eds.) GD 2010. LNCS, vol. 6502, pp. 50–61. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-18469-7_5 CrossRefGoogle Scholar
  3. 3.
    Bach, B., Pietriga, E., Fekete, J.D.: GraphDiaries: animated transitions and temporal navigation for dynamic networks. IEEE Trans. Vis. Comput. Graphics 20(5), 740–754 (2014)CrossRefGoogle Scholar
  4. 4.
    Beck, F., Burch, M., Diehl, S., Weiskopf, D.: The state of the art in visualizing dynamic graphs. In: Proceedings of EuroVis - STARs (2014)Google Scholar
  5. 5.
    Bremm, S., Von Landesberger, T., Heß, M., Schreck, T., Weil, P., Hamacher, K.: Interactive visual comparison of multiple trees. In: Proceedings of IEEE VAST, pp. 31–40 (2011)Google Scholar
  6. 6.
    Chaparro, B.S., Hinkle, V.D., Riley, S.K.: The usability of computerized card sorting: a comparison of three applications by researchers and end users. J. Usability Stud. 4(1), 31–48 (2008)Google Scholar
  7. 7.
    Collins, C.M., Carpendale, S.: VisLink: revealing relationships amongst visualizations. IEEE Trans. Vis. Comput. Graph. 13(6), 1192–1199 (2007)CrossRefGoogle Scholar
  8. 8.
    Dwyer, T., Lee, B., Fisher, D., Quinn, K.I., Isenberg, P., Robertson, G., North, C.: A comparison of user-generated and automatic graph layouts. IEEE Trans. Vis. Comput. Graph. 15(6), 961–968 (2009)CrossRefGoogle Scholar
  9. 9.
    Fuchs, J., Isenberg, P., Bezerianos, A., Fischer, F., Bertini, E.: The influence of contour on similarity perception of star glyphs. IEEE Trans. Vis. Comput. Graph. 20(12), 2251–2260 (2014)CrossRefGoogle Scholar
  10. 10.
    Gao, X., Xiao, B., Tao, D., Li, X.: A survey of graph edit distance. Pattern Anal. Appl. 13(1), 113–129 (2010)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Ghani, S., Elmqvist, N., Yi, J.S.: Perception of animated node-link diagrams for dynamic graphs. Comput. Graph. Forum 31(3), 1205–1214 (2012)CrossRefGoogle Scholar
  12. 12.
    Gleicher, M., Albers, D., Walker, R., Jusufi, I., Hansen, C.D., Roberts, J.C.: Visual comparison for information visualization. Inf. Vis. 10(4), 289–309 (2011)CrossRefGoogle Scholar
  13. 13.
    Goldstone, R.L., Son, J.Y.: Similarity. In: Holyoak, K.J., Morrison, R.G. (Eds.) The Cambridge Handbook of Thinking and Reasoning (2005)Google Scholar
  14. 14.
    Greve, G.: Different or alike? comparing computer-based and paper-based card sorting. Int. J. Strateg. Innovative Mark. 1(1), 27–36 (2014)Google Scholar
  15. 15.
    Hadlak, S., Schumann, H., Schulz, H.J.: A survey of multi-faceted graph visualization. In: Proceedings of EuroVis - STARs (2015)Google Scholar
  16. 16.
    Holten, D., Van Wijk, J.J.: Visual comparison of hierarchically organized data. Comput. Graph. Forum 27(3), 759–766 (2008)CrossRefGoogle Scholar
  17. 17.
    Holten, D., van Wijk, J.J.: A user study on visualizing directed edges in graphs. In: Proceedings of CHI, pp. 2299–2308 (2009)Google Scholar
  18. 18.
    Huang, W., Hong, S.-H., Eades, P.: Layout effects on sociogram perception. In: Healy, P., Nikolov, N.S. (eds.) GD 2005. LNCS, vol. 3843, pp. 262–273. Springer, Heidelberg (2006).  https://doi.org/10.1007/11618058_24 CrossRefGoogle Scholar
  19. 19.
    Jeh, G., Widom, J.: SimRank: a measure of structural-context similarity. In: Proceedings of KDD, pp. 538–543 (2002)Google Scholar
  20. 20.
    Kieffer, S., Dwyer, T., Marriott, K., Wybrow, M.: HOLA: Human-like orthogonal network layout. IEEE Trans. Vis. Comput. Graph. 22(1), 349–358 (2016)CrossRefGoogle Scholar
  21. 21.
    Kleinberg, J.M.: Authoritative sources in a hyperlinked environment. J. ACM 46(5), 604–632 (1999)MathSciNetCrossRefzbMATHGoogle Scholar
  22. 22.
    Klippel, A., Hardisty, F., Weaver, C.: Star plots: how shape characteristics influence classification tasks. Cartogr. Geogr. Inf. Sci. 36(2), 149–163 (2009)CrossRefGoogle Scholar
  23. 23.
    Kobourov, S.G., Pupyrev, S., Saket, B.: Are crossings important for drawing large graphs? In: Duncan, C., Symvonis, A. (eds.) GD 2014. LNCS, vol. 8871, pp. 234–245. Springer, Heidelberg (2014).  https://doi.org/10.1007/978-3-662-45803-7_20 Google Scholar
  24. 24.
    Körner, C.: Concepts and misconceptions in comprehension of hierarchical graphs. Learn. Instr. 15(4), 281–296 (2005)CrossRefGoogle Scholar
  25. 25.
    von Landesberger, T., Kuijper, A., Schreck, T., Kohlhammer, J., van Wijk, J., Fekete, J.D., Fellner, D.: Visual analysis of large graphs: State-of-the-art and future research challenges. Comput. Graph. Forum 30(6), 1719–1749 (2011)CrossRefGoogle Scholar
  26. 26.
    von Landesberger, T., Diel, S., Bremm, S., Fellner, D.W.: Visual analysis of contagion in networks. Inf. Vis. 14(2), 93–110 (2015)CrossRefGoogle Scholar
  27. 27.
    von Landesberger, T., Pohl, M., Wallner, G., Distler, M., Ballweg, K.: Investigating graph similarity perception: a preliminary study and methodological challenges. In: Proceedings of VISIGRAPP, pp. 241–250 (2017)Google Scholar
  28. 28.
    Lenz, O., Keul, F., Bremm, S., Hamacher, K., von Landesberger, T.: Visual analysis of patterns in multiple amino acid mutation graphs. In: Proceedings of IEEE VAST, pp. 93–102 (2014)Google Scholar
  29. 29.
    McGee, F., Dingliana, J.: An empirical study on the impact of edge bundling on user comprehension of graphs. In: Proceedings of AVI, pp. 620–627 (2012)Google Scholar
  30. 30.
    McGrath, C., Blythe, J., Krackhardt, D.: The effect of spatial arrangement on judgments and errors in interpreting graphs. Soc. Netw. 19(3), 223–242 (1997)CrossRefGoogle Scholar
  31. 31.
    Melnik, S., Garcia-Molina, H., Rahm, E.: Similarity flooding: a versatile graph matching algorithm and its application to schema matching. In: Proceedings of ICDE, pp. 117–128 (2002)Google Scholar
  32. 32.
    Novick, L.R.: The importance of both diagrammatic conventions and domain-specific knowledge for diagram literacy in science: the hierarchy as an illustrative case. In: Barker-Plummer, D., Cox, R., Swoboda, N. (eds.) Diagrams 2006. LNCS (LNAI), vol. 4045, pp. 1–11. Springer, Heidelberg (2006).  https://doi.org/10.1007/11783183_1 CrossRefGoogle Scholar
  33. 33.
    Pandey, A.V., Krause, J., Felix, C., Boy, J., Bertini, E.: Towards understanding human similarity perception in the analysis of large sets of scatter plots. In: Proceedings of CHI, pp. 3659–3669 (2016)Google Scholar
  34. 34.
    Pekalska, E., Duin, R.P.W.: The dissimilarity representation for pattern recognition: Foundations and applications (2005)Google Scholar
  35. 35.
    Purchase, H.C., Pilcher, C., Plimmer, B.: Graph drawing aesthetics - created by users, not algorithms. IEEE Trans. Vis. Comput. Graph. 18(1), 81–92 (2012)CrossRefGoogle Scholar
  36. 36.
    Purchase, H.: Which aesthetic has the greatest effect on human understanding? In: DiBattista, G. (ed.) GD 1997. LNCS, vol. 1353, pp. 248–261. Springer, Heidelberg (1997).  https://doi.org/10.1007/3-540-63938-1_67 CrossRefGoogle Scholar
  37. 37.
    Purchase, H.C.: Metrics for graph drawing aesthetics. Vis. Lang. Comput. 13(5), 501–516 (2002)CrossRefGoogle Scholar
  38. 38.
    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).  https://doi.org/10.1007/978-3-540-70904-6_19 CrossRefGoogle Scholar
  39. 39.
    Purchase, H.C., McGill, M., Colpoys, L., Carrington, D.: Graph drawing aesthetics and the comprehension of UML class diagrams: An empirical study. In: Proceedings of Invis.au. pp. 129–137 (2001)Google Scholar
  40. 40.
    Tennekes, M., de Jonge, E.: Tree colors: color schemes for tree-structured data. IEEE Trans. Vis. Comput. Graph. 20(12), 2072–2081 (2014)CrossRefGoogle Scholar
  41. 41.
    Thornley, S., Marshall, R., Wells, S., Jackson, R.: Using directed acyclic graphs for investigating causal paths for cardiovascular disease. J. Biometrics Biostatistics 4, 182 (2013)CrossRefGoogle Scholar
  42. 42.
    Tibshirani, R., Walther, G., Hastie, T.: Estimating the number of clusters in a data set via the gap statistic. J. R. Stat. Soc. Ser. B Stat. Methodol. 63(2), 411–423 (2001)MathSciNetCrossRefzbMATHGoogle Scholar
  43. 43.
    Tominski, C., Forsell, C., Johansson, J.: Interaction support for visual comparison inspired by natural behavior. IEEE Trans. Vis. Comput. Graph. 18(12), 2719–2728 (2012)CrossRefGoogle Scholar
  44. 44.
    Vehlow, C., Beck, F., Weiskopf, D.: The state of the art in visualizing group structures in graphs. In: Proceedings of EuroVis - STARs (2015)Google Scholar
  45. 45.
    Welch, E., Kobourov, S.: Measuring symmetry in drawings of graphs. Comput. Graph. Forum 36(3), 341–351 (2017)CrossRefGoogle Scholar
  46. 46.
    Wood, J.R., Wood, L.E.: Card sorting: current practices and beyond. J. Usability Stud. 4(1), 1–6 (2008)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • K. Ballweg
    • 1
  • M. Pohl
    • 2
  • G. Wallner
    • 2
  • T. von Landesberger
    • 1
  1. 1.Technische Universität DarmstadtDarmstadtGermany
  2. 2.Vienna University of TechnologyViennaAustria

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