Skip to main content
SpringerLink
Log in

How Do You Connect Moving Dots? Insights from User Studies on Dynamic Network Visualizations

  • Chapter
  • First Online:
Book cover Handbook of Human Centric Visualization

Abstract

In recent years, the analysis of dynamic network data has become an increasingly prominent research issue. While several visual analytics techniques with the focus on the examination of temporal evolving networks have been proposed in recent years, their effectiveness and utility for end users need to be further analyzed. When dealing with techniques for dynamic network analysis, which integrate visual, computational, and interactive components, users become easily overwhelmed by the amount of information displayed—even in case of small sized networks. Therefore we evaluated visual analytics techniques for dynamic networks during their development, performing intermediate evaluations by means of mock-up and eye-tracking studies and a final evaluation of the running interactive prototype, traceing three pathways of development in detail: The first one focused on the maintenance of the user’s mental map throughout changes of network structure over time, changes caused by user interactions, and changes of analytical perspectives. The second one addresses the avoidance of visual clutter, or at least its moderation. The third pathway of development follows the implications of unexpected user behaviour and multiple problem solving processes. Aside from presenting solutions based on the outcomes of our evaluation, we discuss open and upcoming problems and set out new research questions.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Ahn, J., Plaisant, C., Shneiderman, B.: A task taxonomy of network evolution analysis. Tech Report HCIL-2011-09 (2011)

    Google Scholar 

  2. Aigner, W., Miksch, S., Schumann, H., Tominski, C.: Visualization of Time-Oriented Data, 1st edn. Human-Computer Interaction. Springer Verlag (2011)

    Google Scholar 

  3. Andrews, K., Wohlfahrt, M., Wurzinger, G.: Visual graph comparison. In: Information Visualisation, 2009 13th International Conference, pp. 62–67 (2009)

    Google Scholar 

  4. Bertini, E., Perer, A., Plaisant, C., Santucci, G.: Beliv’08: Beyond time and errors: novel evaluation methods for information visualization. In: CHI’08 extended abstracts on Human factors in computing systems, pp. 3913–3916. ACM (2008)

    Google Scholar 

  5. Bier, E. A., Stone, M. C., Pier, K., Buxton, W., DeRose, T. D.: Toolglass and magic lenses: the see-through interface. Proceedings of the 20th annual conference on Computer graphics and interactive techniques (S. 73–80). ACM (1993)

    Google Scholar 

  6. Brandes, U., Corman, S.: Visual unrolling of network evolution and the analysis of dynamic discourse. Information Visualization 2(1), 40–50 (2003)

    Article  Google Scholar 

  7. Brandes, U., Indlekofer, N., Mader, M.: Visualization methods for longitudinal social networks and stochastic actor oriented modeling. Social Networks (2011)

    Google Scholar 

  8. Chang, R., Ziemkiewicz, C., Green, T. M., & Ribarsky, W.: Defining insight for visual analytics. Computer Graphics and Applications, IEEE, 29(2), 14–17 (2009)

    Article  Google Scholar 

  9. Chase, W. G., Simon, H. A. (1973). Perception in chess. Cognitive Psychology, 4, 55–81 (1973)

    Article  Google Scholar 

  10. Correa, C.D., Ma, K.L.: Visualizing social networks. In: C.C. Aggarwal (ed.) Social Network Data Analytics, pp. 307–326. Springer US (2011)

    Google Scholar 

  11. Di Battista, G., Eades, P., Tamassia, R., Tollis, I.G.: Algorithms for drawing graphs: an annotated bibliography. Computational Geometry 4(5), 235–282 (1994)

    Article  MathSciNet  MATH  Google Scholar 

  12. Dwyer, T., Gallagher, D.R.: Visualising changes in fund manager holdings in two and a halfdimensions. Information Visualization 3(4), 227–244 (2004)

    Article  Google Scholar 

  13. Eades, P., Lai, W., Misue, K., Sugiyama, K.: Preserving the mental map of a diagram. Technical Report IIAS-RR-91-16E, International Institute for Advanced Study of Social Information Science, Fujitsu Laboratories Ltd. (1991)

    Google Scholar 

  14. Ellis, G., Dix, A.: An explorative analysis of user evaluation studies in information visualisation. 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 

  15. Federico, P., Aigner, W., Miksch, S., Windhager, F., Zenk, L.: A visual analytics approach to dynamic social networks. In: Proceedings of the 11th International Conference on Knowledge Management and Knowledge Technologies, i-KNOW 11, pp. 47:1–47:8. ACM, New York, NY, USA (2011)

    Google Scholar 

  16. Friedrich, C., Houle, M.: Graph drawing in motion ii. In: P. Mutzel, M. Jnger, S. Leipert (eds.) Graph Drawing, Lecture Notes in Computer Science, vol. 2265, pp. 122–125. Springer Berlin / Heidelberg (2002)

    Google Scholar 

  17. Freeman, L. C.: Visualizing social networks. Journal of social structure, 1(1), 4 (2000)

    Google Scholar 

  18. Henderson, J. M.: Regarding scenes. Current Directions in Psychological Science 16, pp. 219–222 (2007)

    Article  Google Scholar 

  19. Helsen, W. F., Starkes, J. L.: A multidimensional approach to skilled perception and performance in sport. Applied Cognitive Psychology, 13, pp. 1–27 (1999)

    Article  Google Scholar 

  20. Herman, I., Melancon, G., Marshall, M.: Graph visualization and navigation in information visualization: A survey. Visualization and Computer Graphics, IEEE Transactions on 6(1), 24–43 (2000)

    Google Scholar 

  21. Isenberg, P., Tang, A., Carpendale, S.: An exploratory study of visual information analysis. In: Proceeding of the twenty-sixth annual SIGCHI conference on Human factors in computing systems, pp. 1217–1226. ACM (2008)

    Google Scholar 

  22. Isenberg, P., Zuk, T., Collins, C., Carpendale, S.: Grounded evaluation of information visualizations. In: Proceedings of the 2008 conference on BEyond time and errors: novel evaLuation methods for Information Visualization, p. 6. ACM (2008)

    Google Scholar 

  23. Jonassen, D.: Instructional design models for well-structured and ill-structured problem-solving learning outcomes. Educational Technology Research and Development 45(1), 65–94 (1997).

    Article  Google Scholar 

  24. Just, M. A., Carpenter, P. A.: Eye fixations and cognitive processes. Cognitive Psychology 8(1), 441–480, (1976)

    Article  Google Scholar 

  25. Kilduff, M., Brass, D.: Organizational social network research: Core ideas and key debates. The Academy of Management Annals 4(1), 317–357 (2010)

    Article  Google Scholar 

  26. Körner, C. (2004). Sequential processing in comprehension of hierarchical graphs. Applied Cognitive Psychology, 18(4), 467–480. doi:10.1002/acp.997

    Article  Google Scholar 

  27. Landauer, T., Prabhu, P., Helander, M.: Handbook of Human-Computer Interaction. Elsevier Science Inc. (1998)

    Google Scholar 

  28. Mahapatra, D., Winkler, S., Yen, S. C.: Motion saliency outweighs other low-level features while watching videos. In Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, 6806, pp. 68060P1-68060P10 (2008)

    Google Scholar 

  29. Marchionini, G.: Exploratory search: from fnding to understanding. Communications of the ACM 49(4), 41–46 (2006)

    Article  Google Scholar 

  30. Mayr, E., Knipfer, K., Wessel, D.: In-sights into mobile learning. An exploration of mobile eye tracking methodology for learning in museums. In G. Vavoula, N. Pachter, & A. Kukulska-Hulme (Eds.), Researching mobile learning: Frameworks, methods, and research designs, pp. 189–204. Oxford, UK: Peter Lang (2009)

    Google Scholar 

  31. Mayr, E., Smuc, M., Risku, H.: Many roads lead to rome: Mapping users problem-solving strategies. Information Visualization 10(3), 232–247 (2011)

    Article  Google Scholar 

  32. Misue, K., Eades, P., Lai, W., Sugiyama, K.: Layout adjustment and the mental map. Journal of Visual Languages and Computing 6(2), 183–210 (1995)

    Article  Google Scholar 

  33. Moody, J., McFarland, D., Bender-deMoll, S.: Dynamic network visualization. American Journal of Sociology 110(4), 1206–1241 (2005)

    Article  Google Scholar 

  34. Perer, A., Shneiderman, B.: Integrating statistics and visualization: case studies of gaining clarity during exploratory data analysis. In: Proceedings of the twenty-sixth annual SIGCHI conference on Human factors in computing systems, CHI 08, pp. 265–274. ACM, New York, NY, USA (2008)

    Google Scholar 

  35. Plaisant, C.: The challenge of information visualization evaluation. In: Proceedings of the working conference on Advanced visual interfaces, pp. 109–116. ACM (2004)

    Google Scholar 

  36. Poole, A., Ball, L. J.:. Eye tracking in human-computer interaction and usability research: current status and future prospects. Encyclopedia of human computer interaction, pp. 211–219 (2005)

    Google Scholar 

  37. Rosenholtz, R., Li, Y., Mansfeld, J., Jin, Z.: Feature congestion: a measure of display clutter. In: Proceedings of the SIGCHI conference on Human factors in computing systems, CHI ’05, pp. 761–770. ACM, New York, NY, USA (2005)

    Google Scholar 

  38. Saffrey, P., Purchase, H.: The “mental map” versus “static aesthetic” compromise in dynamic graphs: a user study. In: Proceedings of the ninth conference on Australasian user interface - Volume 76, AUIC 08, pp. 85–93. Australian Computer Society, Inc., Darlinghurst, Australia (2008)

    Google Scholar 

  39. Saraiya, P., North, C., Duca, K.: An insight-based methodology for evaluating bioinformatics visualizations. Visualization and Computer Graphics, IEEE Transactions on 11(4), 443–456 (2005)

    Google Scholar 

  40. Shneiderman, B., Plaisant, C.: Strategies for evaluating information visualization tools: multidimensional in-depth long-term case studies. 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 

  41. Springmeyer, R., Blattner, M., Max, N.: A characterization of the scienti?c data analysis process. In: Proceedings of the 3rd conference on Visualization’92, pp. 235–242. IEEE Computer Society Press (1992)

    Google Scholar 

  42. Trafton, J. G., Marshall, S., Mintz, F., & Trickett, S. B.: Extracting and implicit information from complex visualizations (pp. 206–220). In M. Hegarty, B. Meyer, & H. Narayanan (Eds.) Diagrammatic Representation and Inference. Diagrammatic Representation and Inference (2002)

    Google Scholar 

  43. Tufte, E. R.: The Visual Display of Quantitative Information. Graphics Press, Cheshire, CT (1983)

    Google Scholar 

  44. von Landesberger, T., Kuijper, A., Schreck, T., Kohlhammer, J., Van Wijk, J., Fekete, J., & Fellner, D.: Visual analysis of large graphs. Proceedings of Euro-Graphics: State of the Art Report, 2 (2010)

    Google Scholar 

  45. Wasserman, S., Faust, K.: Social Network Analysis: Methods and Applications, 1 edn. No. 8 in Structural analysis in the social sciences. Cambridge University Press (1994)

    Google Scholar 

  46. Ware, C.: Information Visualization - Perception for Design, Morgan Kaufmann (2004)

    Google Scholar 

  47. Whiting, M., Haack, J., Varley, C.: Creating realistic, scenario-based synthetic data for test and evaluation of information analytics software. In: Proceedings of the 2008 conference on BEyond time and errors: novel evaLuation methods for Information Visualization, p. 8. ACM (2008)

    Google Scholar 

  48. Windhager, F., Zenk, L., Federico, P.: Visual enterprise network analytics-visualizing organizational change. Procedia-Social and Behavioral Sciences 22, 59–68 (2011).

    Article  Google Scholar 

Download references

Acknowledgements

This research was supported by the Austrian FFG research program FIT-IT Visual Computing (No. 820928, Research project ViENA, Visual Enterprise Network Analytics, http://fitit-viena.org) and the Centre for Visual Analytics Science and Technology (No. 822746, CVAST, http://www.cvast.tuwien.ac.at) funded by the Austrian Federal Ministry of Economy, Family and Youth in the exceptional Laura Bassi Centres of Excellence initiative. For their support of the prototype evaluation we want to particularly thank the algorithmics research group at Konstanz University, Department of Computer and Information Science, Prof. Ulrik Brandes.

Author information

Authors and Affiliations

  1. Department for Knowledge and Communication Management, Danube University Krems, Dr.-Karl-Dorrek-StraĂźe 30, 3500, Krems, Austria

    Michael Smuc, Florian Windhager & Lukas Zenk

  2. Institute of Software Technology and Interactive Systems, Vienna University of Technology, Favoritenstrasse 9-11/188, 1040, Vienna, Austria

    Paolo Federico, Wolfgang Aigner & Silvia Miksch

Authors
  1. Michael Smuc
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Paolo Federico
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Florian Windhager
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wolfgang Aigner
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Lukas Zenk
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Silvia Miksch
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Michael Smuc .

Editor information

Editors and Affiliations

  1. CSIRO ICT Centre, Marsfield, New South Wales, Australia

    Weidong Huang

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer Science+Business Media New York

About this chapter

Cite this chapter

Smuc, M., Federico, P., Windhager, F., Aigner, W., Zenk, L., Miksch, S. (2014). How Do You Connect Moving Dots? Insights from User Studies on Dynamic Network Visualizations. In: Huang, W. (eds) Handbook of Human Centric Visualization. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7485-2_25

Download citation

  • DOI: https://doi.org/10.1007/978-1-4614-7485-2_25

  • Published:

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4614-7484-5

  • Online ISBN: 978-1-4614-7485-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation