Immersive Dynamic Visualization of Interactions in a Social Network

  • Nicolas Greffard
  • Fabien Picarougne
  • Pascale Kuntz
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

This paper is focused on the visualization of dynamic social networks, i.e. graphs whose edges model social relationships which evolve during time. In order to overcome the problem of discontinuities of the graphical representations computed by discrete methods, the proposed approach is a continuous one which updates the changes as soon as they happen in the visual restitution. The vast majority of the continuous approaches are restricted to 2D supports which do not optimally match the human perception capabilities. We here present TempoSpring which is a new interactive 3D visualization tool of dynamic graphs. This innovative tool relies on a force-directed layout method to span the 3D space along with several immersive setups (active stereoscopic system/visualization in a dome) to offer an efficient user-experience. TempoSpring has initially been developed in a particular application context: the analysis of sociability networks in the French medieval peasant society.

References

  1. Archambault D, Munzner T, Auber D (2007) Topolayout: Multi-level graph layout by topological features. IEEE Trans Visual Comput Graph 13:2007CrossRefGoogle Scholar
  2. Di-Battista G, Eades P, Tamassia R, Tollis IG (1999) Graph drawing - algorithms for the visualization of graphs. Prentice Hall, Upper Saddle River, NJMATHGoogle Scholar
  3. Falkowski T, Bartelheimer J, Spiliopoulou M (2006) Mining and visualizing the evolution of subgroups in social networks. In: WI ’06: Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence, IEEE Computer Society, pp 52–58Google Scholar
  4. Fernanda B, Viégas, Donath J (2004) Social network visualization: Can we go beyond the graph. In: Workshop on Social Networks for Design and Analysis: Using Network Information in CSCW 2004, pp 6–10Google Scholar
  5. Freeman L (2000) Visualizing social networks. J Soc Struct 1:151–161Google Scholar
  6. Fruchterman TMJ, Reingold EM (1991) Graph drawing by force-directed placement. Software Pract Ex 21(11):1129–1164CrossRefGoogle Scholar
  7. Ghoniem M, Fekete JD, Castagliola P (2005) On the readability of graphs using node-link and matrix-based representations: a controlled experiment and statistical analysis. Inform Visual 4(2):114–135CrossRefGoogle Scholar
  8. Halpin H, Zielinski D, Brady R, Kelly G (2008) Exploring semantic social networks using virtual reality. In: Proceedings of the 7th International Conference on The Semantic Web, Lecture Notes In Computer Science, vol 5318, pp 599–614Google Scholar
  9. Herman I, Melançon G, Marshall S (2000) Graph visualization and navigation in information visualization: a survey. IEEE Trans Visual Comput Graph 6(1):24–43CrossRefGoogle Scholar
  10. Knuth D (1963) Computer-drawn flowcharts. Comm ACM 6:555–563CrossRefGoogle Scholar
  11. McCrickard DS, Kehoe CM (1997) Visualizing search results using sqwid. In: Sixth International World Wide Web Conference, ACM Press, pp 51–60Google Scholar
  12. Misue K, Eades P, Lai W, Sugiyama K (1995) Layout adjustment and the mental map. J Visual Lang Comput 6(2):183–210CrossRefGoogle Scholar
  13. Puolamäki K, Bertone A (2009) Introduction to the special issue on visual analytics and knowledge discovery. SIGKDD Explorations 11(2):3–4CrossRefGoogle Scholar
  14. Rosvall M, Bergstrom CT (2010) Mapping change in large networks. PLoS ONE 5(1)Google Scholar
  15. Sarkar M, Brown MH (1992) Graphical fisheye views of graphs. In: CHI ’92: Proceedings of the SIGCHI conference on Human factors in computing systems, ACM, pp 83–91Google Scholar
  16. Telea A, Ersoy O (2010) Image-based edge bundles: Simplified visualization of large graphs. Comput Graph Forum 29(3):843–852CrossRefGoogle Scholar
  17. Ware C, Mitchell P (2008) Visualizing graphs in three dimensions. ACM Trans Appl Percept 5:2–15CrossRefGoogle Scholar
  18. Wasserman S, Faust K (1994) Social network analysis: methods and applications. Cambridge University Press, Cambridge, New YorkCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Nicolas Greffard
    • 1
  • Fabien Picarougne
    • 1
  • Pascale Kuntz
    • 1
  1. 1.LINA, Polytech’NantesNANTES Cedex 3France

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