B02 | Adaptive Network Visualization

Prof. Ulrik Brandes, Universität Konstanz
Email | Website

Ulrik Brandes

Prof. Daniel Weiskopf, Universität Stuttgart
Email | Website

Daniel Weiskopf

Mereke van Garderen – Website | E-Mail

We plan to extend stress-minimization approaches for general graph layout. Current algorithms fail on specific classes of input graphs, for instance because of scale, diameter, or skewed degree distributions. Moreover, characteristics of the output device and user interactions are generally ignored. Adaptive algorithms shall be developed from quantitative descriptions of the effects of graph characteristics on layout features. Since extensive algorithmic experimentation will be required to understand empirically the response curves of such algorithms, we will also contribute to methodology in experimental algorithmics.

Research Questions

What are appropriate means of adaptation in network information visualization?

Can we make reliable quantitative predictions about the relation between adaptations and layout effects?

Which experiment-design techniques from other disciplines can be adopted to improve the study of algorithm behavior?

Can we improve the parametrization of graph layout algorithms? 

Standard layout.

Cohesion adapted layout.

Publications

  1. M. van Garderen, B. Pampel, A. Nocaj, and U. Brandes, “Minimum-Displacement Overlap Removal for Geo-referenced Data Visualization,” The Author(s) Computer Graphics Forum, vol. 36, no. 3, pp. 423–433, 2017.
  2. J. Hildenbrand, A. Nocaj, and U. Brandes, “Flexible Level-of-Detail Rendering for Large Graphs,” no. 9801 2016, G. Drawing and 24th International Symposium Network Visualization, Eds. 2016.
  3. A. Nocaj, M. Ortmann, and U. Brandes, “Adaptive Disentanglement based on Local Clustering in Small-World Network Visualization,” IEEE Transactions on Visualization and Computer Graphics, vol. 22, no. 6, pp. 1662–1671, 2016.
  4. C. Schulz, A. Nocaj, J. Goertler, O. Deussen, U. Brandes, and D. Weiskopf, “Probabilistic Graph Layout for Uncertain Network Visualization,” vol. 23, no. 1, 2017.
  5. C. Schulz, A. Nocaj, M. El-Assady, S. Frey, M. Hlawatsch, M. Hund, G. K. Karch, R. Netzel, C. Schätzle, M. Butt, D. A. Keim, T. Ertl, U. Brandes, and D. Weiskopf, “Generative Data Models for Validation and Evaluation of Visualization Techniques.,” in BELIV Workshop 2016, 2016, pp. 112–124.

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