Temporal Multivariate Networks

  • Daniel Archambault
  • James Abello
  • Jessie Kennedy
  • Stephen Kobourov
  • Kwan-Liu Ma
  • Silvia Miksch
  • Chris Muelder
  • Alexandru C. Telea
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8380)

Abstract

Networks that evolve over time, or dynamic graphs, have been of interest to the areas of information visualization and graph drawing for many years. Typically, the structure of the dynamic graph evolves as vertices and edges are added or removed from the graph. In a multivariate scenario, however, attributes play an important role and can also evolve over time. In this chapter, we characterize and survey methods for visualizing temporal multivariate networks. We also explore future applications and directions for this emerging area in the fields of information visualization and graph drawing.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Abello, J., Hadlak, S., Schumann, H., Schulz, H.: A modular degree-of-interest specification for the visual analysis of large dynamic networks. IEEE Transactions on Visualization and Computer Graphics (in press, 2014)Google Scholar
  2. 2.
    Aigner, W., Miksch, S., Schumann, H., Tominski, C.: Visualization of Time-Oriented Data. Springer, London (2011)CrossRefGoogle Scholar
  3. 3.
    Andrienko, N., Andrienko, G.: Exploratory Analysis of Spatial and Temporal Data: A Systematic Approach. Springer, Berlin (2006)MATHGoogle Scholar
  4. 4.
    Archambault, D., Purchase, H.C.: The mental map and memorability in dynamic graphs. In: Hauser, H., Kobourov, S.G., Qu, H. (eds.) Proc. of the IEEE Pacific Visualization Symposium, pp. 89–96. IEEE (2012)Google Scholar
  5. 5.
    Archambault, D., Purchase, H.C.: The “map” in the mental map: Experimental results in dynamic graph drawing. International Journal of Human-Computer Studies 71(11), 1044–1055 (2013)CrossRefMATHGoogle Scholar
  6. 6.
    Archambault, D., Purchase, H.C.: Mental map preservation helps user orientation in dynamic graphs. In: Didimo, W., Patrignani, M. (eds.) GD 2012. LNCS, vol. 7704, pp. 475–486. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  7. 7.
    Archambault, D., Purchase, H.C., Pinaud, B.: Animation, small multiples, and the effect of mental map preservation in dynamic graphs. IEEE Transactions on Visualization and Computer Graphics 17(4), 539–552 (2011)CrossRefMATHGoogle Scholar
  8. 8.
    Barsky, A., Munzner, T., Gardy, J., Kincaid, R.: Cerebral: Visualizing multiple experimental conditions on a graph with biological context. IEEE Transactions on Visualization and Computer Graphics 14(6), 1253–1260 (2008)CrossRefGoogle Scholar
  9. 9.
    Bastian, M., Heymann, S., Jacomy, M.: Gephi: an open source software for exploring and manipulating networks. In: International AAAI Conference on Weblogs and Social Media, pp. 361–362 (2009)Google Scholar
  10. 10.
    Bender-deMoll, S., McFarland, D.A.: The art and science of dynamic network visualization. Journal of Social Structure 7(2) (2006)Google Scholar
  11. 11.
    Bettini, C., Jajodia, S., Wang, S.X.: Time Granularities in Databases, Data Mining, and Temporal Reasoning. Springer, Berlin (2000)CrossRefMATHGoogle Scholar
  12. 12.
    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
  13. 13.
    Boitmanis, K., Brandes, U., Pich, C.: Visualizing internet evolution on the autonomous systems level. In: Hong, S.-H., Nishizeki, T., Quan, W. (eds.) GD 2007. LNCS, vol. 4875, pp. 365–376. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  14. 14.
    Brandes, U., Corman, S.R.: Visual unrolling of network evolution and the analysis of dynamic discourse. In: Proc. of the IEEE Symposium on Information Visualization, pp. 145–151 (2002)Google Scholar
  15. 15.
    Brandes, U., Fleischer, D., Puppe, T.: Dynamic spectral layout with an application to small worlds. Journal of Graph Algorithms and Applications 11(2), 325–343 (2007)MathSciNetCrossRefMATHGoogle Scholar
  16. 16.
    Brandes, U., Indlekofer, N., Mader, M.: Visualization methods for longitudinal social networks and stochastic actor-oriented modeling. Social Networks, 291–308 (June 2011)Google Scholar
  17. 17.
    Brandes, U., Mader, M.: A quantitative comparison of stress-minimization approaches for offline dynamic graph drawing. In: Speckmann, B. (ed.) GD 2011. LNCS, vol. 7034, pp. 99–110. Springer, Heidelberg (2011)Google Scholar
  18. 18.
    Brandes, U., Pich, C.: An experimental study on distance-based graph drawing. In: Tollis, I.G., Patrignani, M. (eds.) GD 2008. LNCS, vol. 5417, pp. 218–229. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  19. 19.
    Brandes, U., Wagner, D.: A Bayesian paradigm for dynamic graph layout. In: DiBattista, G. (ed.) GD 1997. LNCS, vol. 1353, pp. 236–247. Springer, Heidelberg (1997)CrossRefGoogle Scholar
  20. 20.
    Branke, J.: Dynamic graph drawing. In: Kaufmann, M., Wagner, D. (eds.) Drawing Graphs. LNCS, vol. 2025, pp. 228–246. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  21. 21.
    Burch, M., Vehlow, C., Beck, F., Diehl, S., Weiskopf, D.: Parallel edge splatting for scalable dynamic graph visualization. IEEE Transactions on Visualization and Computer Graphics 17(12), 2344–2353 (2011)CrossRefGoogle Scholar
  22. 22.
    Byelas, H., Telea, A.: Visualization of areas of interest in software architecture diagrams. In: ACM SoftVis 2006, pp. 105–114 (2006)Google Scholar
  23. 23.
    Cohen, J.D.: Drawing graphs to convey proximity: An incremental arrangement method. ACM Transactions on Computer-Human Interaction 4(3), 197–229 (1997)CrossRefGoogle Scholar
  24. 24.
    Collberg, C., Kobourov, S.G., Nagra, J., Pitts, J., Wampler, K.: A system for graph-based visualization of the evolution of software. In: ACM SoftVis 2003, pp. 77–86 (2003)Google Scholar
  25. 25.
    Collins, C., Penn, G., Carpendale, S.: Bubble sets: Revealing set relations with isocontours over existing visualizations. IEEE Transactions on Visualization and Computer Graphics 15(6), 1009–1016 (2009)CrossRefGoogle Scholar
  26. 26.
    Crnovrsanin, T., Liao, I., Wuy, Y., Ma, K.L.: Visual recommendations for network navigation. In: Proc. of the 13th Eurographics/IEEE - VGTC Conference on Visualization, EuroVis 2011, pp. 1081–1090. Eurographics Association, Aire-la-Ville (2011)Google Scholar
  27. 27.
    Cui, W., Liu, S., Tan, L., Shi, C., Song, Y., Gao, Z., Qu, H., Tong, X.: Textflow: Towards better understanding of evolving topics in text. IEEE Transactions on Visualization and Computer Graphics 17(12), 2412–2421 (2011)CrossRefGoogle Scholar
  28. 28.
    Diehl, S.: Software Visualization: Visualizing the Structure, Behaviour, and Evolution of Software. Springer, Berlin (2010)MATHGoogle Scholar
  29. 29.
    Diehl, S., Görg, C.: Graphs, they are changing. In: Goodrich, M.T., Kobourov, S.G. (eds.) GD 2002. LNCS, vol. 2528, pp. 23–30. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  30. 30.
    Dux, B., Iyer, A., Debray, S.K., Forrester, D., Kobourov, S.G.: Visualizing the behavior of dynamically modifiable code. In: IWPC, pp. 337–340 (2005)Google Scholar
  31. 31.
    Dwyer, T., Gallagher, D.R.: Visualising changes in fund manager holdings in two and a half-dimensions. Information Visualization 3, 227–244 (2004)CrossRefGoogle Scholar
  32. 32.
    Dwyer, T.: Extending the WilmaScope 3D Graph Visualisation System – Software Demonstration. In: Hong, S.H. (ed.) APVIS. CRPIT, vol. 45, pp. 39–45. Australian Computer Society (2005)Google Scholar
  33. 33.
    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 (2009)CrossRefGoogle Scholar
  34. 34.
    Erten, C., Kobourov, S., Le, V., Navabi, A.: Simultaneous graph drawing: layout algorithms and visualization schemes. Journal of Graph Algorithms and Applications 9(1), 165–182 (2005)MathSciNetCrossRefMATHGoogle Scholar
  35. 35.
    Erten, C., Harding, P.J., Kobourov, S.G., Wampler, K., Yee, G.: Exploring the computing literature using temporal graph visualization. In: Electronic Imaging 2004, pp. 45–56 (2004)Google Scholar
  36. 36.
    Erten, C., Harding, P.J., Kobourov, S.G., Wampler, K., Yee, G.: GraphAEL: Graph animations with evolving layouts. In: Liotta, G. (ed.) GD 2003. LNCS, vol. 2912, pp. 98–110. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  37. 37.
    Farrugia, M., Quigley, A.: Cell phone mini challenge: Node-link animation award animating multivariate dynamic social networks. In: IEEE Visual Analytics Science and Technology, pp. 215–216 (October 2008)Google Scholar
  38. 38.
    Farrugia, M., Quigley, A.: Effective temporal graph layout: A comparative study of animation versus static display methods. Journal of Information Visualization 10(1), 47–64 (2011)Google Scholar
  39. 39.
    Feng, K.C., Wang, C., Shen, H.W., Lee, T.Y.: Coherent time-varying graph drawing with multi-focus+context interaction. IEEE Transactions on Visualization and Computer Graphics (2011)Google Scholar
  40. 40.
    Forrester, D., Kobourov, S.G., Navabi, A., Wampler, K., Yee, G.V.: Graphael: A system for generalized force-directed layouts. In: Pach, J. (ed.) GD 2004. LNCS, vol. 3383, pp. 454–464. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  41. 41.
    Frank, A.U.: Different Types of “Times” in GIS. In: Egenhofer, M.J., Golledge, R.G. (eds.) Spatial and Temporal Reasoning in Geographic Information Systems, pp. 40–62. Oxford University Press, New York (1998)Google Scholar
  42. 42.
    Frishman, Y., Tal, A.: Online dynamic graph drawing. IEEE Transactions on Visualization and Computer Graphics 14, 727–740 (2008)CrossRefGoogle Scholar
  43. 43.
    Frishman, Y., Tal, A.: Dynamic drawing of clustered graphs. In: Proc. of the IEEE Symposium on Information Visualization, pp. 191–198. IEEE Computer Society, Washington, DC (2004)CrossRefGoogle Scholar
  44. 44.
    Fruchterman, T.M.J., Reingold, E.M.: Graph drawing by force-directed placement. Software - Practice and Experience 21(11), 1129–1164 (1991)CrossRefGoogle Scholar
  45. 45.
    Furnas, G.W.: Generalized fisheye views. In: Human Factors in Computing Systems CHI, pp. 16–23 (1986)Google Scholar
  46. 46.
    Gajer, P., Kobourov, S.G.: GRIP: Graph drawing with intelligent placement. In: Marks, J. (ed.) GD 2000. LNCS, vol. 1984, pp. 222–228. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  47. 47.
    Gamma, E., Helm, R., Johnson, R., Vlissides, J.: Design Patterns: Elements of Reusable Object-Oriented Software. Addison-Wesley (1994)Google Scholar
  48. 48.
    Gansner, E.R., Hu, Y., North, S.C.: A maxent-stress model for graph layout. In: Proc. of the IEEE Pacific Visualization Symposium, pp. 73–80 (2012)Google Scholar
  49. 49.
    Görg, C., Birke, P., Pohl, M., Diehl, S.: Dynamic graph drawing of sequences of orthogonal and hierarchical graphs. In: Pach, J. (ed.) GD 2004. LNCS, vol. 3383, pp. 228–238. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  50. 50.
    Görke, R., Hartmann, T., Wagner, D.: Dynamic graph clustering using minimum-cut trees. In: Dehne, F., Gavrilova, M., Sack, J.-R., Tóth, C.D. (eds.) WADS 2009. LNCS, vol. 5664, pp. 339–350. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  51. 51.
    Görke, R., Maillard, P., Staudt, C., Wagner, D.: Modularity-driven clustering of dynamic graphs. In: Festa, P. (ed.) SEA 2010. LNCS, vol. 6049, pp. 436–448. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  52. 52.
    Hachul, S.: A Potential-Field-Based Multilevel Algorithm for Drawing Large Graphs. Ph.D. thesis, Universität zu Köln (2002)Google Scholar
  53. 53.
    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
  54. 54.
    Harel, D., Koren, Y.: Graph drawing by high-dimensional embedding. In: Goodrich, M.T., Kobourov, S.G. (eds.) GD 2002. LNCS, vol. 2528, pp. 207–219. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  55. 55.
    Harrower, M.: Tips for designing effective animated maps. Cartographic Perspectives 44, 63–65 (2003)CrossRefGoogle Scholar
  56. 56.
    Heer, J., Boyd, D.: Vizster: visualizing online social networks. In: Proc. of the IEEE Symposium on Information Visualization, pp. 32–39 (2005)Google Scholar
  57. 57.
    Hoogendorp, H., Ersoy, O., Reniers, D., Telea, A.: Extraction and visualization of call dependencies for large C/C++ code bases: A comparative study. In: Proc. ACM VISSOFT, pp. 137–145 (2009)Google Scholar
  58. 58.
    Hu, Y., Gansner, E.R., Kobourov, S.G.: Visualizing graphs and clusters as maps. IEEE Computer Graphics and Applications 30(6), 54–66 (2010)CrossRefGoogle Scholar
  59. 59.
    Hu, Y., Kobourov, S.G., Veeramoni, S.: Embedding, clustering and coloring for dynamic maps. In: Proc. of the IEEE Pacific Visualization Symposium, pp. 33–40 (2012)Google Scholar
  60. 60.
    Inselberg, A.: Parallel Coordinates: Visual Multidimensional Geometry and Its Applications. Springer (2009)Google Scholar
  61. 61.
    Jaramillo, C.M.Z., Gelbukh, A., Isaza, F.A.: Pre-conceptual schema: A conceptual-graph-like knowledge representation for requirements elicitation. In: Gelbukh, A., Reyes-Garcia, C.A. (eds.) MICAI 2006. LNCS (LNAI), vol. 4293, pp. 27–37. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  62. 62.
    Joia, P., Paulovich, F.V., Coimbra, D., Cuminato, J.A., Nonato, L.G.: Local affine multidimensional projection. IEEE Transactions on Visualization and Computer Graphics 17, 2563–2571 (2011)CrossRefGoogle Scholar
  63. 63.
    Kamada, T., Kawai, S.: An algorithm for drawing general undirected graphs. Inf. Process. Lett. 31(1), 7–15 (1989)MathSciNetCrossRefMATHGoogle Scholar
  64. 64.
    Kim, N.W., Card, S.K., Heer, J.: Tracing genealogical data with timenets. In: Proc. of the International Conference on Advanced Visual Interfaces, AVI 2010, pp. 241–248. ACM, New York (2010)Google Scholar
  65. 65.
    Koren, Y., Carmel, L., Harel, D.: ACE: A fast multiscale eigenvectors computation for drawing huge graphs. In: Proc. of the IEEE Symposium on Information Visualization, pp. 137–145 (2002)Google Scholar
  66. 66.
    Kumar, G., Garland, M.: Visual exploration of complex time-varying graphs. IEEE Transactions on Visualization and Computer Graphics 12(5), 805–812 (2006)CrossRefGoogle Scholar
  67. 67.
    Lanza, M., Marinescu, R.: Object-Oriented Metrics in Practice - Using Software Metrics to Characterize, Evaluate, and Improve the Design of Object-Oriented Systems. Springer (2006)Google Scholar
  68. 68.
    Lyons, K.A.: Cluster busting in anchored graph drawing. In: CASCON, pp. 7–17 (1992)Google Scholar
  69. 69.
    Mashima, D., Kobourov, S.G., Hu, Y.: Visualizing dynamic data with maps. IEEE Transactions on Visualization and Computer Graphics 18(9), 1424–1437 (2012)CrossRefGoogle Scholar
  70. 70.
    Mens, T., Demeyer, S.: Software Evolution. Springer (2008)Google Scholar
  71. 71.
    Moody, J., McFarland, D., Bender-DeMoll, S.: Dynamic network visualization. American Journal of Sociology 110(4), 1206–1241 (2005)CrossRefGoogle Scholar
  72. 72.
    Moreta, S., Telea, A.: Multiscale visualization of dynamic software logs. In: Proc. Eurovis, pp. 11–18 (2007)Google Scholar
  73. 73.
    Moscovich, T., Chevalier, F., Henry, N., Pietriga, E., Fekete, J.D.: Topology-Aware Navigation in Large Networks. In: SIGCHI Conference on Human Factors in Computing Systems, pp. 2319–2328 (2009)Google Scholar
  74. 74.
    Muelder, C., Ma, K.L.: Rapid graph layout using space filling curves. IEEE Transactions on Visualization and Computer Graphics 14(6), 1301–1308 (2008)CrossRefGoogle Scholar
  75. 75.
    Muelder, C., Ma, K.L.: A treemap based method for rapid layout of large graphs. In: Proc. of the IEEE Pacific Visualization Symposium, pp. 231–238 (2008)Google Scholar
  76. 76.
    Muelder, C.W., Crnovrsanin, T., Ma, K.L.: Egocentric storylines for visual analysis of large dynamic graphs. In: Proc. of 1st IEEE Workshop on Big Data Visualization (BigDataVis), pp. 56–62 (October 2013)Google Scholar
  77. 77.
    Xkcd #657: Movie narrative charts (December 2009), http://xkcd.com/657
  78. 78.
    Noack, A.: An energy model for visual graph clustering. In: Liotta, G. (ed.) GD 2003. LNCS, vol. 2912, pp. 425–436. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  79. 79.
    North, S.C.: Incremental layout in DynaDAG. In: Brandenburg, F.J. (ed.) GD 1995. LNCS, vol. 1027, pp. 409–418. Springer, Heidelberg (1996)CrossRefGoogle Scholar
  80. 80.
    Ogawa, M., Ma, K.L.: Software evolution storylines. In: Proc. of the International Symposium on Software Visualization (SoftVis 2010), pp. 35–42. ACM, New York (2010)CrossRefGoogle Scholar
  81. 81.
    Ogievetsky, V.: Plotweaver xkcd/657 creation tool (March 2009), https://graphics.stanford.edu/wikis/cs448b-09-fall/FPOgievetskyVadim
  82. 82.
    Orso, A., Jones, J., Harrold, M.J.: Visualization of program-execution data for deployed software. In: Proc. ACM SOFTVIS, pp. 67–75 (2003)Google Scholar
  83. 83.
    Paulovich, F., Eler, D., Poco, J., Botha, C., Minghim, R., Nonato, L.G.: Piece wise Laplacian-based projection for interactive data exploration and organization. Computer Graphics Forum 30(3), 1091–1100 (2011)CrossRefGoogle Scholar
  84. 84.
    Paulovich, F.V., Nonato, L.G., Minghim, R., Levkowitz, H.: Least square projection: A fast high-precision multidimensional projection technique and its application to document mapping. IEEE Transactions on Visualization and Computer Graphics 14(3), 564–575 (2008)CrossRefGoogle Scholar
  85. 85.
    Paulovich, F.V., Silva, C., Nonato, L.G.: Two-phase mapping for projecting massive data sets. IEEE Transactions on Visualization and Computer Graphics 16, 1281–1290 (2010)CrossRefGoogle Scholar
  86. 86.
    Pfleeger, S.L., Atlee, J.M.: Software Engineering: Theory and Practice, 4th edn. Prentice Hall (2009)Google Scholar
  87. 87.
    Pretorius, A., van Wijk, J.: Visual inspection of multivariate graphs. Computer Graphics Forum 27(3), 967–974 (2008)CrossRefGoogle Scholar
  88. 88.
    Purchase, H., Samra, A.: Extremes are better: Investigating mental map preservation in dynamic graphs. In: Stapleton, G., Howse, J., Lee, J. (eds.) Diagrams 2008. LNCS (LNAI), vol. 5223, pp. 60–73. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  89. 89.
    Reda, K., Tantipathananandh, C., Johnson, A., Leigh, J., Berger-Wolf, T.: Visualizing the evolution of community structures in dynamic social networks. Computer Graphics Forum 30(3), 1061–1070 (2011)CrossRefGoogle Scholar
  90. 90.
    Robertson, G., Fernandez, R., Fisher, D., Lee, B., Stasko, J.: Effectiveness of animation in trend visualization. IEEE Transactions on Visualization and Computer Graphics 14, 1325–1332 (2008)CrossRefGoogle Scholar
  91. 91.
    Rufiange, S., McGuffin, M.J.: DiffAni: Visualizing dynamic graphs with a hybrid of difference maps and animation. IEEE Transactions on Visualization and Computer Graphics 19(12), 2556–2565 (2013)CrossRefGoogle Scholar
  92. 92.
    Rumbaugh, J., Jacobson, I., Booch, G.: The Unified Modeling Language Reference Manual, 2nd edn. Addison-Wesley (2004)Google Scholar
  93. 93.
    Saffrey, P., Purchase, H.: The “mental map” versus “static aesthetic” compromise in dynamic graphs: A user study. In: Proc. of the 9th Australasian User Interface Conference (AUIC2008), pp. 85–93 (2008)Google Scholar
  94. 94.
    Saha, B., Mitra, P.: Dynamic algorithm for graph clustering using minimum cut tree. In: SDM, pp. 581–586. SIAM (2007)Google Scholar
  95. 95.
    Sallaberry, A., Muelder, C., Ma, K.-L.: Clustering, visualizing, and navigating for large dynamic graphs. In: Didimo, W., Patrignani, M. (eds.) GD 2012. LNCS, vol. 7704, pp. 487–498. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  96. 96.
    Schaeffer, S.E.: Graph clustering. Computer Science Review 1(1), 27–64 (2007)CrossRefMATHGoogle Scholar
  97. 97.
    Simonetto, P., Auber, D., Archambault, D.: Fully automatic visualisation of overlapping sets. Computer Graphics Forum 28(3), 967–974 (2009)CrossRefGoogle Scholar
  98. 98.
    Skupin, A., Fabrikant, S.I.: Spatialization methods: a cartographic research agenda for non-geographic information visualization. Cartography and Geographic Information Science 30, 95–119 (2003)CrossRefGoogle Scholar
  99. 99.
    Tanahashi, Y., Ma, K.L.: Design considerations for optimizing storyline visualizations. IEEE Transactions on Visualization and Computer Graphics 18(12), 2679–2688 (2012)CrossRefGoogle Scholar
  100. 100.
    Telea, A., Voinea, L.: An interactive reverse engineering environment for large-scale C++ code. In: Proc. ACM SOFTVIS, pp. 67–76 (2008)Google Scholar
  101. 101.
    Telea, A., Voinea, L., Sassenburg, H.: Visual tools for software architecture understanding: A stakeholder perspective. IEEE Software 27(6), 46–53 (2010)CrossRefGoogle Scholar
  102. 102.
    Tufte, E.R.: Envisionning Information. Graphics Press (1990)Google Scholar
  103. 103.
    Wise, J.A., Thomas, J.J., Pennock, K., Lantrip, D., Pottier, M., Schur, A., Crow, V.: Visualizing the non-visual: spatial analysis and interaction with information from text documents. In: Proc. of the IEEE Symposium on Information Visualization, pp. 51–58 (1995)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Daniel Archambault
  • James Abello
  • Jessie Kennedy
  • Stephen Kobourov
  • Kwan-Liu Ma
  • Silvia Miksch
  • Chris Muelder
  • Alexandru C. Telea

There are no affiliations available

Personalised recommendations