Revisited Experimental Comparison of Node-Link and Matrix Representations

  • Mershack Okoe
  • Radu JianuEmail author
  • Stephen Kobourov
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10692)


Visualizing network data is applicable in domains such as biology, engineering, and social sciences. We report the results of a study comparing the effectiveness of the two primary techniques for showing network data: node-link diagrams and adjacency matrices. Specifically, an evaluation with a large number of online participants revealed statistically significant differences between the two visualizations. Our work adds to existing research in several ways. First, we explore a broad spectrum of network tasks, many of which had not been previously evaluated. Second, our study uses a large dataset, typical of many real-life networks not explored by previous studies. Third, we leverage crowdsourcing to evaluate many tasks with many participants.


  1. 1.
    Abuthawabeh, A., Beck, F., Zeckzer, D., Diehl, S.: Finding structures in multi-type code couplings with node-link and matrix visualizations. In: 2013 First IEEE Working Conference on Software Visualization (VISSOFT), pp. 1–10. IEEE (2013)Google Scholar
  2. 2.
    Ahn, Y.Y., Ahnert, S.E., Bagrow, J.P., Barabási, A.L.: Flavor network and the principles of food pairing. Scientific reports 1 (2011)Google Scholar
  3. 3.
    Alper, B., Bach, B., Henry Riche, N., Isenberg, T., Fekete, J.D.: Weighted graph comparison techniques for brain connectivity analysis. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 483–492. ACM (2013)Google Scholar
  4. 4.
    Amar, R., Eagan, J., Stasko, J.: Low-level components of analytic activity in information visualization. In: 2005 IEEE Symposium on Information Visualization, INFOVIS 2005, pp. 111–117. IEEE (2005)Google Scholar
  5. 5.
    Archambault, D., Purchase, H.C., Hoßfeld, T.: Evaluation in the Crowd: Crowdsourcing and Human-Centred Experiments. Springer, Cham (2017). CrossRefGoogle Scholar
  6. 6.
    Auber, D.: Tulip: a huge graph visualization framework. In: Jünger, M., Mutzel, P. (eds.) Graph Drawing Software, pp. 105–126. Springer, Heidelberg (2004). CrossRefGoogle Scholar
  7. 7.
    Bach, B., Pietriga, E., Fekete, J.D.: Visualizing dynamic networks with matrix cubes. In: Proceedings of the 32nd Annual ACM Conference on Human Factors in Computing Systems, pp. 877–886. ACM (2014)Google Scholar
  8. 8.
    Barsky, A., Gardy, J.L., Hancock, R.E., Munzner, T.: Cerebral: a cytoscape plugin for layout of and interaction with biological networks using subcellular localization annotation. Bioinformatics 23(8), 1040–1042 (2007)CrossRefGoogle Scholar
  9. 9.
    Bastian, M., Heymann, S., Jacomy, M., et al.: Gephi: an open source software for exploring and manipulating networks. ICWSM 8, 361–362 (2009)Google Scholar
  10. 10.
    Behrisch, M., Davey, J., Fischer, F., Thonnard, O., Schreck, T., Keim, D., Kohlhammer, J.: Visual analysis of sets of heterogeneous matrices using projection-based distance functions and semantic zoom. In: Computer Graphics Forum, vol. 33, pp. 411–420. Wiley Online Library (2014)Google Scholar
  11. 11.
    Bezerianos, A., Dragicevic, P., Fekete, J.D., Bae, J., Watson, B.: Geneaquilts: a system for exploring large genealogies. IEEE Trans. Vis. Comput. Graph. 16(6), 1073–1081 (2010)CrossRefGoogle Scholar
  12. 12.
    Blanch, R., Dautriche, R., Bisson, G.: Dendrogramix: a hybrid tree-matrix visualization technique to support interactive exploration of dendrograms. In: 2015 IEEE Pacific Visualization Symposium (PacificVis), pp. 31–38. IEEE (2015)Google Scholar
  13. 13.
    Borkin, M., Gajos, K., Peters, A., Mitsouras, D., Melchionna, S., Rybicki, F., Feldman, C., Pfister, H.: Evaluation of artery visualizations for heart disease diagnosis. IEEE Trans. Vis. Comput. Graph. 17(12), 2479–2488 (2011)CrossRefGoogle Scholar
  14. 14.
    Borkin, M., Vo, A., Bylinskii, Z., Isola, P., Sunkavalli, S., Oliva, A., Pfister, H., et al.: What makes a visualization memorable? IEEE Trans. Vis. Comput. Graph. 19(12), 2306–2315 (2013)CrossRefGoogle Scholar
  15. 15.
    Chapman, P., Stapleton, G., Rodgers, P., Micallef, L., Blake, A.: Visualizing sets: an empirical comparison of diagram types. In: Dwyer, T., Purchase, H., Delaney, A. (eds.) Diagrams 2014. LNCS (LNAI), vol. 8578, pp. 146–160. Springer, Heidelberg (2014). Google Scholar
  16. 16.
    Christensen, J., Bae, J.H., Watson, B., Rappa, M.: Understanding which graph depictions are best for viewers. In: Christie, M., Li, T.-Y. (eds.) SG 2014. LNCS, vol. 8698, pp. 174–177. Springer, Cham (2014). Google Scholar
  17. 17.
    Dinkla, K., Westenberg, M.A., van Wijk, J.J.: Compressed adjacency matrices: untangling gene regulatory networks. IEEE Trans. Vis. Comput. Graph. 18(12), 2457–2466 (2012)CrossRefGoogle Scholar
  18. 18.
    Ellson, J., Gansner, E., Koutsofios, L., North, S.C., Woodhull, G.: Graphviz—open source graph drawing tools. In: Mutzel, P., Jünger, M., Leipert, S. (eds.) GD 2001. LNCS, vol. 2265, pp. 483–484. Springer, Heidelberg (2002). CrossRefGoogle Scholar
  19. 19.
    Elmqvist, N., Do, T.N., Goodell, H., Henry, N., Fekete, J.D.: Zame: interactive large-scale graph visualization. In: 2008 IEEE Pacific Visualization Symposium, PacificVIS 2008, pp. 215–222. IEEE (2008)Google Scholar
  20. 20.
    Fekete, J.D.: Reorder.js: a javascript library to reorder tables and networks. In: IEEE VIS 2015 (2015)Google Scholar
  21. 21.
    Ghoniem, M., Fekete, J.D., Castagliola, P.: A comparison of the readability of graphs using node-link and matrix-based representations. In: 2004 IEEE Symposium on Information Visualization, INFOVIS 2004, pp. 17–24. IEEE (2004)Google Scholar
  22. 22.
    Ghoniem, M., Fekete, J.D., Castagliola, P.: On the readability of graphs using node-link and matrix-based representations: a controlled experiment and statistical analysis. Inf. Vis. 4(2), 114–135 (2005)CrossRefGoogle Scholar
  23. 23.
    Heer, J., Bostock, M.: Crowdsourcing graphical perception: using mechanical turk to assess visualization design. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 203–212. ACM (2010)Google Scholar
  24. 24.
    Henry, N., Fekete, J.D.: Matrixexplorer: a dual-representation system to explore social networks. IEEE Trans. Vis. Comput. Graph. 12(5), 677–684 (2006)CrossRefGoogle Scholar
  25. 25.
    Hu, Y., Gansner, E., Kobourov, S.G.: Visualizing graphs and clusters as maps. IEEE Comput. Graph. Appl. 30(6), 54–66 (2010)CrossRefGoogle Scholar
  26. 26.
    Huang, W.: Using eye tracking to investigate graph layout effects. In: 2007 6th International Asia-Pacific Symposium on Visualization, APVIS 2007, pp. 97–100. IEEE (2007)Google Scholar
  27. 27.
    Huang, W., Eades, P., Hong, S.H.: Measuring effectiveness of graph visualizations: a cognitive load perspective. Inf. Vis. 8(3), 139–152 (2009)CrossRefGoogle Scholar
  28. 28.
    Jianu, R., Rusu, A., Hu, Y., Taggart, D.: How to display group information on node-link diagrams: an evaluation. IEEE Trans. Vis. Comput. Graph. 20(11), 1530–1541 (2014)CrossRefGoogle Scholar
  29. 29.
    Jourdan, F., Melançon, G.: Tool for metabolic and regulatory pathways visual analysis. In: Electronic Imaging 2003, pp. 46–55. International Society for Optics and Photonics (2003)Google Scholar
  30. 30.
    Keller, R., Eckert, C.M., Clarkson, P.J.: Matrices or node-link diagrams: which visual representation is better for visualising connectivity models? Inf. Vis. 5(1), 62–76 (2006)CrossRefGoogle Scholar
  31. 31.
    Kosara, R., Ziemkiewicz, C.: Do mechanical turks dream of square pie charts? In: Proceedings of the 3rd BELIV 2010 Workshop: Beyond Time and Errors: Novel Evaluation Methods for Information Visualization, pp. 63–70. ACM (2010)Google Scholar
  32. 32.
    Lee, B., Plaisant, C., Parr, C.S., Fekete, J.D., Henry, N.: Task taxonomy for graph visualization. In: Proceedings of the 2006 AVI Workshop on Beyond Time and Errors: Novel Evaluation Methods for Information Visualization, pp. 1–5. ACM (2006)Google Scholar
  33. 33.
    Mason, W., Suri, S.: Conducting behavioral research on Amazons mechanical turk. Behav. Res. Methods 44(1), 1–23 (2012)CrossRefGoogle Scholar
  34. 34.
    Melancon, G.: Just how dense are dense graphs in the real world?: a methodological note. In: Proceedings of the 2006 AVI Workshop on Beyond Time and Errors: Novel Evaluation Methods for Information Visualization, pp. 1–7. ACM (2006)Google Scholar
  35. 35.
    Micallef, L., Dragicevic, P., Fekete, J.D.: Assessing the effect of visualizations on bayesian reasoning through crowdsourcing. IEEE Trans. Vis. Comput. Graph. 18(12), 2536–2545 (2012)CrossRefGoogle Scholar
  36. 36.
    Okoe, M., Jianu, R.: Ecological validity in quantitative user studies-a case study in graph evaluation (2015)Google Scholar
  37. 37.
    Okoe, M., Jianu, R.: Graphunit: evaluating interactive graph visualizations using crowdsourcing. In: Computer Graphics Forum, vol. 34, pp. 451–460. Wiley Online Library (2015)Google Scholar
  38. 38.
    Paolacci, G., Chandler, J., Ipeirotis, P.G.: Running experiments on Amazon mechanical turk. Judgment Decis. Making 5(5), 411–419 (2010)Google Scholar
  39. 39.
    Perin, C., Dragicevic, P., Fekete, J.D.: Revisiting bertin matrices: new interactions for crafting tabular visualizations. IEEE Trans. Vis. Comput. Graph. 20(12), 2082–2091 (2014)CrossRefGoogle Scholar
  40. 40.
    Purchase, H.: Which aesthetic has the greatest effect on human understanding? In: Graph Drawing. pp. 248–261. Springer, Heidelberg (1997).
  41. 41.
    Purchase, H.C., Cohen, R.F., James, M.: Validating graph drawing aesthetics. In: Brandenburg, F.J. (ed.) GD 1995. LNCS, vol. 1027, pp. 435–446. Springer, Heidelberg (1996). CrossRefGoogle Scholar
  42. 42.
    Robertson, G., Fernandez, R., Fisher, D., Lee, B., Stasko, J.: Effectiveness of animation in trend visualization. IEEE Trans. Vis. Comput. Graph. 14(6) (2008)Google Scholar
  43. 43.
    Rodgers, P., Stapleton, G., Chapman, P.: Visualizing sets with linear diagrams. ACM Trans. Comput.-Hum. Interact. (TOCHI) 22(6), 27 (2015)CrossRefGoogle Scholar
  44. 44.
    Ross, J., Irani, L., Silberman, M., Zaldivar, A., Tomlinson, B.: Who are the crowdworkers?: shifting demographics in mechanical turk. In: CHI 2010 Extended Abstracts on Human Factors in Computing Systems, pp. 2863–2872. ACM (2010)Google Scholar
  45. 45.
    Rufiange, S., McGuffin, M.J., Fuhrman, C.P.: Treematrix: a hybrid visualization of compound graphs. In: Computer Graphics Forum, vol. 31, pp. 89–101. Wiley Online Library (2012)Google Scholar
  46. 46.
    Saket, B., Scheidegger, C., Kobourov, S., Börner, K.: Map-based visualizations increase recall accuracy of data. Comput. Graph. Forum 34(3), 441–450 (2015)CrossRefGoogle Scholar
  47. 47.
    Saket, B., Simonetto, P., Kobourov, S.: Group-level graph visualization taxonomy. arXiv preprint arXiv:1403.7421 (2014)
  48. 48.
    Saket, B., Simonetto, P., Kobourov, S., Borner, K.: Node, node-link, and node-link-group diagrams: an evaluation. IEEE Trans. Vis. Comput. Graph. 20(12), 2231–2240 (2014)CrossRefGoogle Scholar
  49. 49.
    Sedlmair, M., Isenberg, P., Baur, D., Mauerer, M., Pigorsch, C., Butz, A.: Cardiogram: visual analytics for automotive engineers. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1727–1736. ACM (2011)Google Scholar
  50. 50.
    Shannon, P., Markiel, A., Ozier, O., Baliga, N.S., Wang, J.T., Ramage, D., Amin, N., Schwikowski, B., Ideker, T.: Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13(11), 2498–2504 (2003)CrossRefGoogle Scholar
  51. 51.
    Sheny, Z., Maz, K.L.: Path visualization for adjacency matrices. In: Proceedings of the 9th Joint Eurographics/IEEE VGTC conference on Visualization, pp. 83–90. Eurographics Association (2007)Google Scholar
  52. 52.
    Shneiderman, B.: The eyes have it: a task by data type taxonomy for information visualizations. In: 1996 Proceedings of the IEEE Symposium on Visual Languages, pp. 336–343. IEEE (1996)Google Scholar
  53. 53.
    Viégas, F.B., Donath, J.: Social network visualization: can we go beyond the graph. In: Workshop on Social Networks, CSCW, vol. 4, pp. 6–10 (2004)Google Scholar
  54. 54.
    Von Landesberger, T., Kuijper, A., Schreck, T., Kohlhammer, J., van Wijk, J.J., Fekete, J.D., Fellner, D.W.: Visual analysis of large graphs: state-of-the-art and future research challenges. In: Computer Graphics Forum, vol. 30, pp. 1719–1749. Wiley Online Library (2011)Google Scholar
  55. 55.
    Ware, C., Purchase, H., Colpoys, L., McGill, M.: Cognitive measurements of graph aesthetics. Inf. Vis. 1(2), 103–110 (2002)CrossRefGoogle Scholar
  56. 56.
    Yi, J.S., Elmqvist, N., Lee, S.: Timematrix: analyzing temporal social networks using interactive matrix-based visualizations. Int. J. Hum.-Comput. Interact. 26(11–12), 1031–1051 (2010)CrossRefGoogle Scholar
  57. 57.
    Ziemkiewicz, C., Kosara, R.: The shaping of information by visual metaphors. IEEE Trans. Vis. Comput. Graph. 14(6) (2008)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceFlorida International UniversityMiamiUSA
  2. 2.giCentreCity, University of LondonLondonUK
  3. 3.Department of Computer ScienceUniversity of ArizonaTucsonUSA

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