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Navigation Recommendations for Exploring Hierarchical Graphs

  • Stefan Gladisch
  • Heidrun Schumann
  • Christian Tominski
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8034)

Abstract

Navigation is a key interaction when analyzing graphs by means of interactive visualization. Particularly for unknown graphs, the user often faces situations where it is not entirely clear where to go next. For hierarchical graphs, the user may also ponder whether it is useful to look at the data at a higher or lower level of abstraction.

In this paper, we present a novel approach for recommending places in a hierarchical graph that are worth visiting next. A flexible definition of interestingness based on the notion of a degree of interest (DOI) allows us to recommend horizontal navigation in terms of the graph layout and also vertical navigation in terms of the level of abstraction. The actual recommendation is communicated to the user through unobtrusive visual cues that are embedded into the visual representation of the graph. A proof-of-concept implementation has been integrated into an existing graph visualization system.

Keywords

Information Visualization Exploration State Graph Visualization Partial View Graph Layout 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Herman, I., Melançon, G., Marshall, M.S.: Graph Visualization and Navigation in Information Visualization: a Survey. IEEE Transactions on Visualization and Computer Graphics 6, 24–43 (2000)Google Scholar
  2. 2.
    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. Research Report RR-7860, INRIA (2012)Google Scholar
  3. 3.
    Mathieu, B., Heymann, S., Jacomy, M.: Gephi: An Open Source Software for Exploring and Manipulating Networks. In: Proceedings of the International Conference on Weblogs and Social Media (ICWSM), pp. 361–362. Association for the Advancement of Artificial Intelligence (2009)Google Scholar
  4. 4.
    Tominski, C., Abello, J., Schumann, H.: CGV – An Interactive Graph Visualization System. Computers & Graphics 33, 660–678 (2009)CrossRefGoogle Scholar
  5. 5.
    Spence, R.: Information Visualization: Design for Interaction, 2nd edn. Prentice-Hall (2007)Google Scholar
  6. 6.
    Abello, J., van Ham, F., Krishnan, N.: ASK-GraphView: A Large Scale Graph Visualization System. IEEE Transactions on Visualization and Computer Graphics 12, 669–676 (2006)CrossRefGoogle Scholar
  7. 7.
    Elmqvist, N., Fekete, J.D.: Hierarchical Aggregation for Information Visualization: Overview, Techniques, and Design Guidelines. IEEE Transactions on Visualization and Computer Graphics 16, 439–454 (2010)CrossRefGoogle Scholar
  8. 8.
    May, T., Steiger, M., Kohlhammer, J.D.J.: Using Signposts for Navigation in Large Graphs. Computer Graphics Forum 31, 985–994 (2012)CrossRefGoogle Scholar
  9. 9.
    Jusufi, I., Klukas, C., Kerren, A., Schreiber, F.: Guiding the Interactive Exploration of Metabolic Pathway Interconnections. Information Visualization 11, 136–150 (2012)CrossRefGoogle Scholar
  10. 10.
    Plaisant, C., Grosjean, J., Bederson, B.: SpaceTree: Supporting Exploration in Large Node Link Tree, Design Evolution and Empirical Evaluation. In: Proceedings of the IEEE Symposium on Information Visualization (InfoVis), pp. 57–64. IEEE Computer Society (2002)Google Scholar
  11. 11.
    Baudisch, P., Rosenholtz, R.: Halo: A Technique for Visualizing Off-Screen Objects. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI), pp. 481–488. ACM Press (2003)Google Scholar
  12. 12.
    Gustafson, S., Baudisch, P., Gutwin, C., Irani, P.: Wedge: Clutter-Free Visualization of Off-Screen Locations. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI), pp. 787–796. ACM Press (2008)Google Scholar
  13. 13.
    Frisch, M., Dachselt, R.: Visualizing offscreen elements of node-link diagrams. Information Visualization 12, 133–162 (2013)CrossRefGoogle Scholar
  14. 14.
    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, 953–960 (2009)CrossRefGoogle Scholar
  15. 15.
    Crnovrsanin, T., Liao, I., Wu, Y., Ma, K.L.: Visual Recommendations for Network Navigation. Computer Graphics Forum 30, 1081–1090 (2011)CrossRefGoogle Scholar
  16. 16.
    Perer, A., van Ham, F.: Integrating Querying and Browsing in Partial Graph Visualizations. Technical report, IBM Research (2011)Google Scholar
  17. 17.
    Furnas, G.W.: Generalized Fisheye Views. ACM SIGCHI Bulletin 17, 16–23 (1986)CrossRefGoogle Scholar
  18. 18.
    Huang, M.L., Eades, P., Wang, J.: On-line Animated Visualization of Huge Graphs Using a Modified Spring Algorithm. Journal of Visual Languages and Computing 9, 623–645 (1998)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Stefan Gladisch
    • 1
  • Heidrun Schumann
    • 1
  • Christian Tominski
    • 1
  1. 1.Institute for Computer ScienceUniversity of RostockGermany

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