Interaction in the Visualization of Multivariate Networks

  • Michael Wybrow
  • Niklas Elmqvist
  • Jean-Daniel Fekete
  • Tatiana von Landesberger
  • Jarke J. van Wijk
  • Björn Zimmer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8380)

Abstract

Interaction is a vital component in the visualization of multivariate networks. It enables greater amounts of information to be seen and explored than is possible with static visualization. Interaction can also help show the information landscape of the data while still allowing users to find and view areas of interest in greater detail and pivot between these. In this chapter, we first discuss the design space and requirements for interacting with large multivariate data sets. We describe and classify relevant interaction techniques, and give examples of the interactive aspects of multivariate graph visualization systems. We present recommendations and guidelines for designing novel interaction approaches. Finally, we describe the open challenges within the field of multivariate graph visualization as we see them.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Appert, C., Beaudouin-Lafon, M., Mackay, W.E.: Context matters: Evaluating interaction techniques with the CIS model. In: Fincher, S., Markopoulos, P., Moore, D., Ruddle, R. (eds.) People and Computers XVIII Design for Life, pp. 279–295. Springer, London (2005)CrossRefGoogle Scholar
  2. 2.
    Archambault, D., Munzner, T., Auber, D.: GrouseFlocks: Steerable exploration of graph hierarchy space. IEEE Transactions on Visualization and Computer Graphics 14(4), 900–913 (2008)CrossRefGoogle Scholar
  3. 3.
    Auber, D., Archambault, D., Bourqui, R., Lambert, A., Mathiaut, M., Mary, P., Delest, M., Dubois, J.: Melançon, G.: The Tulip 3 Framework: A scalable software library for information visualization applications based on relational data. Technical Report RR-7860, INRIA (January 2012)Google Scholar
  4. 4.
    Becker, R.A., Cleveland, W.S.: Brushing scatterplots. Technometrics 29(2), 127–142 (1987)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Bertini, E., Rigamonti, M., Lalanne, D.: Extended excentric labeling. In: Proceedings of the 11th Eurographics / IEEE – VGTC Conference on Visualization, EuroVis 2009, pp. 927–934. Eurographics Association, Aire-la-Ville (2009)Google Scholar
  6. 6.
    Bezerianos, A., Chevalier, F., Dragicevic, P., Elmqvist, N., Fekete, J.D.: GraphDice: A system for exploring multivariate social networks. Computer Graphics Forum 29(3), 863–872 (2010)CrossRefGoogle Scholar
  7. 7.
    Bier, E.A., Stone, M.C., Pier, K., Buxton, W., DeRose, T.D.: Toolglass and magic lenses: the see-through interface. In: Proceedings of the 20th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH 1993, pp. 73–80. ACM, New York (1993)CrossRefGoogle Scholar
  8. 8.
    Blackwell, A.F., Britton, C., Cox, A.L., Green, T.R.G., Gurr, C.A., Kadoda, G.F., Kutar, M., Loomes, M., Nehaniv, C.L., Petre, M., Roast, C., Roe, C., Wong, A., Young, R.M.: Cognitive dimensions of notations: Design tools for cognitive technology. In: Beynon, M., Nehaniv, C.L., Dautenhahn, K. (eds.) CT 2001. LNCS (LNAI), vol. 2117, pp. 325–341. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  9. 9.
    Buja, A., McDonald, J.A., Michalak, J., Stuetzle, W.: Interactive data visualization using focusing and linking. In: Proceedings of the 2nd Conference on Visualization, VIS 1991, pp. 156–163. IEEE Computer Society Press, Los Alamitos (1991)Google Scholar
  10. 10.
    Card, S., Mackinlay, J., Shneiderman, B.: Readings in Information Visualization: Using Vision to Think. Morgan Kaufmann Publishers (1999)Google Scholar
  11. 11.
    Collins, C., Carpendale, S.: VisLink: Revealing relationships amongst visualizations. IEEE Transactions on Visualization and Computer Graphics 13(6), 1192–1199 (2007)CrossRefGoogle Scholar
  12. 12.
    Dix, A., Finlay, J., Abowd, G.D., Beale, R.: Human-Computer Interaction, 3rd edn. Prentice-Hall, Inc. (2003)Google Scholar
  13. 13.
    Dörk, M., Riche, N.H., Ramos, G., Dumais, S.: PivotPaths: Strolling through faceted information spaces. IEEE Transactions on Visualization and Computer Graphics 18(12), 2709–2718 (2012)CrossRefGoogle Scholar
  14. 14.
    Dunne, C., Riche, N.H., Lee, B., Metoyer, R., Robertson, G.: GraphTrail: Analyzing large multivariate, heterogeneous networks while supporting exploration history. In: Proceedings of the ACM Conference on Human Factors in Computer Systems, pp. 1663–1672 (2012)Google Scholar
  15. 15.
    Dwyer, T., Marriott, K., Schreiber, F., Stuckey, P., Woodward, M., Wybrow, M.: Exploration of networks using overview+detail with constraint-based cooperative layout. IEEE Transactions on Visualization and Computer Graphics 14(6), 1293–1300 (2008)CrossRefGoogle Scholar
  16. 16.
    Dwyer, T., Marriott, K., Wybrow, M.: Dunnart: A constraint-based network diagram authoring tool. In: Tollis, I.G., Patrignani, M. (eds.) GD 2008. LNCS, vol. 5417, pp. 420–431. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  17. 17.
    Elmqvist, N., Do, T.N., Goodell, H., Henry, N., Fekete, J.D.: ZAME: Interactive large-scale graph visualization. In: Proceedings of the IEEE Pacific Symposium on Visualization, pp. 215–222 (2008)Google Scholar
  18. 18.
    Elmqvist, N., Dragicevic, P., Fekete, J.D.: Rolling the dice: Multidimensional visual exploration using scatterplot matrix navigation. IEEE Transactions on Visualization and Computer Graphics 14(6), 1141–1148 (2008)CrossRefGoogle Scholar
  19. 19.
    Elmqvist, N., Fekete, J.D.: Hierarchical aggregation for information visualization: Overview, techniques, and design guidelines. IEEE Transactions on Visualization and Computer Graphics 16(3), 439–454 (2010)CrossRefGoogle Scholar
  20. 20.
    Fekete, J.D., Plaisant, C.: Excentric labeling: dynamic neighborhood labeling for data visualization. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 1999, pp. 512–519. ACM, New York (1999)Google Scholar
  21. 21.
    Fitts, P.M., Peterson, J.R.: Information capacity of discrete motor responses. Journal of Experimental Psychology 67, 103–112 (1964)CrossRefGoogle Scholar
  22. 22.
    Ghani, S., Kwon, B.C., Lee, S., Yi, J.S., Elmqvist, N.: Visual analytics for multimodal social network analysis: A design study with social scientists. IEEE Transactions on Visualization and Computer Graphics 19(12), 2032–2041 (2013)CrossRefGoogle Scholar
  23. 23.
    Gladisch, S., Schumann, H., Tominski, C.: Navigation recommendations for exploring hierarchical graphs. In: 9th International Symposium on Visual Computing (ISVC 2013). Advances in Visual Computing, pp. 36–47. Springer (2013)Google Scholar
  24. 24.
    Gotz, D., Zhou, M.X.: Characterizing users’ visual analytic activity for insight provenance. Information Visualization 8(1), 42–55 (2009)CrossRefGoogle Scholar
  25. 25.
    Green, T.R.G.: Cognitive dimensions of notations. In: Sutcliffe, A., Macaulay, L. (eds.) Proceedings of the 5th Conference of the British Computer Society, Human-Computer Interaction Specialist Group - People and Computers V, pp. 443–460. Cambridge University Press, New York (1989)Google Scholar
  26. 26.
    Hachul, S., Jünger, M.: Large-graph layout algorithms at work: An experimental study. Journal of Graph Algorithms and Applications 11(2), 345–369 (2007)MathSciNetCrossRefMATHGoogle Scholar
  27. 27.
    van Ham, F., Perer, A.: Search, Show Context, Expand on Demand: Supporting large graph exploration with degree-of-interest. IEEE Transactions on Visualization and Computer Graphics 15(6), 953–960 (2009)CrossRefGoogle Scholar
  28. 28.
    Heer, J., Boyd, D.: Vizster: Visualizing online social networks. In: IEEE Symposium on Information Visualization, pp. 32–39 (2005)Google Scholar
  29. 29.
    Heer, J., Card, S.K.: DOITrees revisited: scalable, space-constrained visualization of hierarchical data. In: Proceedings of the ACM Conference on Advanced Visual Interfaces, pp. 421–424 (2004)Google Scholar
  30. 30.
    Heer, J., Shneiderman, B.: Interactive dynamics for visual analysis. Commun. ACM 55(4), 45–54 (2012)CrossRefGoogle Scholar
  31. 31.
    Henry, N., Fekete, J.D.: MatrixExplorer: a dual-representation system to explore social networks. IEEE Transactions on Visualization and Computer Graphics 12(5), 677–684 (2006)CrossRefGoogle Scholar
  32. 32.
    Henry, N., Fekete, J.D., McGuffin, M.J.: NodeTrix: a hybrid visualization of social networks. IEEE Transactions on Visualization and Computer Graphics 13(6), 1302–1309 (2007)CrossRefGoogle Scholar
  33. 33.
    Isenberg, P., Carpendale, S., Bezerianos, A., Henry, N., Fekete, J.D.: CoCoNutTrix: collaborative retrofitting for information visualization. IEEE Comput. Graph. Appl. 29(5), 44–57 (2009)CrossRefGoogle Scholar
  34. 34.
    Jakobsen, M.R., Sahlemariam Haile, Y., Knudsen, S., Hornbaek, K.: Information visualization and proxemics: Design opportunities and empirical findings. IEEE Transactions on Visualization and Computer Graphics 19(12), 2386–2395 (2013)CrossRefGoogle Scholar
  35. 35.
    Jansen, Y., Dragicevic, P.: An interaction model for visualizations beyond the desktop. IEEE Transactions on Visualization and Computer Graphics 19(12), 2396–2405 (2013)CrossRefGoogle Scholar
  36. 36.
    Jusufi, I., Dingjie, Y., Kerren, A.: The Network Lens: Interactive exploration of multivariate networks using visual filtering. In: Proceedings of the 14th International Conference on Information Visualisation (IV 2010). pp. 35–42. IEEE Computer Society (2010)Google Scholar
  37. 37.
    Jusufi, I., Kerren, A., Zimmer, B.: Multivariate network exploration with JauntyNets. In: Proceedings of the 17th International Conference on Information Visualisation (IV 2013), pp. 19–27. IEEE Computer Society Press (2013)Google Scholar
  38. 38.
    Kadivar, N., Chen, V., Dunsmuir, D., Lee, E., Qian, C., Dill, J., Shaw, C., Woodbury, R.: Capturing and supporting the analysis process. In: IEEE Symposium on Visual Analytics Science and Technology, VAST 2009, pp. 131–138 (2009)Google Scholar
  39. 39.
    Kerren, A., Schreiber, F.: Toward the role of interaction in visual analytics. In: Proceedings of the 2012 Winter Simulation Conference (WSC 2012), pp. 420:1–420:13. IEEE Computer Society Press (2012)Google Scholar
  40. 40.
    Kruskal, J., Wish, M.: Multidimensional Scaling, Sage University papers, vol. 11(07). SAGE Publications (1978)Google Scholar
  41. 41.
    Lima, M.: Visual Complexity: Mapping Patterns of Information. Princeton Architectural Press (2011)Google Scholar
  42. 42.
    Liu, Z., Navathe, S.B., Stasko, J.T.: Network-based visual analysis of tabular data. In: IEEE VAST, pp. 41–50 (2011)Google Scholar
  43. 43.
    May, T., Steiger, M., Davey, J., Kohlhammer, J.: Using signposts for navigation in large graphs. Computer Graphics Forum 31(3), 985–994 (2012)CrossRefGoogle Scholar
  44. 44.
    McGuffin, M.J., Jurisica, I.: Interaction techniques for selecting and manipulating subgraphs in network visualizations. IEEE Transactions on Visualization and Computer Graphics 15(6), 937–944 (2009)CrossRefGoogle Scholar
  45. 45.
    Miller, G.A.: The magical number seven, plus or minus two: some limits on our capacity for processing information. Psychological Review 63, 81–97 (1956)CrossRefGoogle Scholar
  46. 46.
    Moscovich, T., Chevalier, F., Henry, N., Pietriga, E., Fekete, J.D.: Topology-aware navigation in large networks. In: Proceedings of the ACM Conference on Human Factors in Computing Systems, pp. 2319–2328 (2009)Google Scholar
  47. 47.
    Munzner, T., Guimbretière, F., Tasiran, S., Zhang, L., Zhou, Y.: TreeJuxtaposer: scalable tree comparison using focus+context with guaranteed visibility. In: ACM SIGGRAPH 2003 Papers, SIGGRAPH 2003, pp. 453–462. ACM (2003)Google Scholar
  48. 48.
    Norman, D.A.: The design of everyday things. Basic Books (2002)Google Scholar
  49. 49.
    North, C., Chang, R., Endert, A., Dou, W., May, R., Pike, B., Fink, G.: Analytic provenance: process+interaction+insight. In: Extended Abstracts of the ACM Conference on Human Factors in Computing Systems, pp. 33–36 (2011)Google Scholar
  50. 50.
    Perlin, K., Fox, D.: Pad: an alternative approach to the computer interface. In: Proceedings of the 20th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH 1993, pp. 57–64. ACM, New York (1993)CrossRefGoogle Scholar
  51. 51.
    Pike, W.A., Stasko, J., Chang, R., O’Connell, T.A.: The science of interaction. Information Visualization 8(4), 263–274 (2009)CrossRefGoogle Scholar
  52. 52.
    Pretorius, A.J., van Wijk, J.J.: Visual inspection of multivariate graphs. In: Proceedings of the Eurographics / IEEE – VGTC Conference on Visualization, pp. 967–974 (2008)Google Scholar
  53. 53.
    Robinson, A.H.: The thematic maps of Charles Joseph Minard. Imago Mundi 21, 95–108 (1967)Google Scholar
  54. 54.
    Roth, R.E.: An empirically-derived taxonomy of interaction primitives for interactive cartography and geovisualization. IEEE Transactions on Visualization and Computer Graphics 19(12), 2356–2365 (2013)CrossRefGoogle Scholar
  55. 55.
    Shneiderman, B.: Direct manipulation: A step beyond programming languages. IEEE Computer 16(8), 57–69 (1983)CrossRefGoogle Scholar
  56. 56.
    Shneiderman, B.: The eyes have it: a task by data type taxonomy for information visualizations. In: Proc. Int. Symp. on Visual Languages, pp. 336–343 (1996)Google Scholar
  57. 57.
    Shneiderman, B., Aris, A.: Network visualization by semantic substrates. IEEE Transactions on Visualization and Computer Graphics 12(5), 733–740 (2006)CrossRefGoogle Scholar
  58. 58.
    Shneiderman, B., Plaisant, C.: Designing the User Interface – Strategies for Effective Human-Computer Interaction, 5th edn. Addison-Wesley (2010)Google Scholar
  59. 59.
    Silva, C., Freire, J., Callahan, S.: Provenance for visualizations: Reproducibility and beyond. Computing in Science Engineering 9(5), 82–89 (2007)CrossRefGoogle Scholar
  60. 60.
    Smoot, M.E., Ono, K., Ruscheinski, J., Wang, P.L., Ideker, T.: Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics 27(3), 431–432 (2011)Google Scholar
  61. 61.
    Spenke, M., Beilken, C., Berlage, T.: FOCUS: the interactive table for product comparison and selection. In: Proceedings of the 9th Annual ACM Symposium on User Interface Software and Technology, UIST 1996, pp. 41–50. ACM, New York (1996)Google Scholar
  62. 62.
    Tekušová, T., Kohlhammer, J.: Visual analysis and exploration of complex corporate shareholder networks. In: Proceedings of the SPIE Conference on Visualization and Data Analysis (VDA 2008), p. 68090F. International Society for Optics and Photonics (2008)Google Scholar
  63. 63.
    Tominski, C., Abello, J., van Ham, F., Schumann, H.: Fisheye tree views and lenses for graph visualization. In: Proceedings of the Internationl Conference on Information Visualization, pp. 17–24 (2006)Google Scholar
  64. 64.
    Tu, Y., Shen, H.W.: Balloon focus: a seamless multi-focus+ context method for treemaps. IEEE Transactions on Visualization and Computer Graphics 14(6), 1157–1164 (2008)CrossRefGoogle Scholar
  65. 65.
    von Landesberger, T., Fiebig, S., Bremm, S., Kuijper, A., Fellner, D.: Interaction taxonomy for tracking of user actions in visual analytics applications. In: Huang, W. (ed.) Handbook of Human-Centric Visualization, pp. 653–670. Springer (2014)Google Scholar
  66. 66.
    von Landesberger, T., Bremm, S., Bernard, J., Schreck, T.: Smart query definition for content-based search in large sets of graphs. In: Proceedings of EuroVAST, pp. 7–12. European Association for Computer Graphics (Eurographics), Eurographics Association, Goslar (2010)Google Scholar
  67. 67.
    von Landesberger, T., Görner, M., Rehner, R., Schreck, T.: A system for interactive visual analysis of large graphs using motifs in graph editing and aggregation. In: Proceedings of the Vision Modeling Visualization Workshop, pp. 331–339 (2009)Google Scholar
  68. 68.
    von Landesberger, T., Kuijper, A., Schreck, T., Kohlhammer, J., van Wijk, J., Fekete, J.D., Fellner, D.W.: Visual analysis of large graphs: State-of-the-art and future research challenges. Computer Graphics Forum 30(6), 1719–1749 (2011)CrossRefGoogle Scholar
  69. 69.
    Wattenberg, M.: Visual exploration of multivariate graphs. In: Proceedings of the ACM Conference on Human Factors in Computing Systems, pp. 811–819 (2006)Google Scholar
  70. 70.
    Weaver, C.: Building highly-coordinated visualizations in Improvise. In: IEEE Symposium on Information Visualization, pp. 159–166 (2004)Google Scholar
  71. 71.
    Wong, N., Carpendale, S., Greenberg, S.: Edgelens: An interactive method for managing edge congestion in graphs. In: Proceedings of the IEEE Symposium on Information Visualization (InfoVis 2003), pp. 51–58. IEEE (2003)Google Scholar
  72. 72.
    Yi, J.S., Kang, Y.A., Stasko, J.T., Jacko, J.A.: Toward a deeper understanding of the role of interaction in information visualization. IEEE Transactions on Visualization and Computer Graphics 13(6), 1224–1231 (2006)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Michael Wybrow
  • Niklas Elmqvist
  • Jean-Daniel Fekete
  • Tatiana von Landesberger
  • Jarke J. van Wijk
  • Björn Zimmer

There are no affiliations available

Personalised recommendations