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

  • Michael Smuc
  • Paolo Federico
  • Florian Windhager
  • Wolfgang Aigner
  • Lukas Zenk
  • Silvia Miksch


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.


Dynamic Network Social Network Analysis Fixation Duration Prototype Evaluation Visual Clutter 
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.



This research was supported by the Austrian FFG research program FIT-IT Visual Computing (No. 820928, Research project ViENA, Visual Enterprise Network Analytics, and the Centre for Visual Analytics Science and Technology (No. 822746, CVAST, 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.


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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Michael Smuc
    • 1
  • Paolo Federico
    • 2
  • Florian Windhager
    • 1
  • Wolfgang Aigner
    • 2
  • Lukas Zenk
    • 1
  • Silvia Miksch
    • 2
  1. 1.Department for Knowledge and Communication ManagementDanube University KremsKremsAustria
  2. 2.Institute of Software Technology and Interactive SystemsVienna University of TechnologyViennaAustria

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