Visual Graph Comparison
The Meanings and Connotations of Visualization Elements
The comparison of networks, i.e., graphs consisting of nodes connected by edges, is often needed in many application areas, for instance biology (the comparison of phylogenetic trees), finance (the assessment of contagion in financial networks, comparison of financial systems), transportation (analysis of changes in transportation networks), or social networks (dynamics of friendship connections, changes in team compositions).
The network comparison is often supported by interactive visualizations. By now various visual comparison techniques have been developed for specific applications. These visualizations convey the information and insights hidden in the data. For conveying information we use various visual elements, e.g. node position, node size. Each encoding should have a specific meaning and displays part of the information and insights in the data (e.g., size only conveys person’s age). The full picture is derived from the combinations of the visual elements (e.g., size and position and color together).
Our encoding efforts might be well elaborated, but there is always the ambiguity of whether the users understand the encoding at all, understand it correctly, understand something we actually did not think about or did not want to encode. This ambiguity is unfavorable, because we want the users to perceive the visualized data correctly without unwanted connotations. In order to come up with possibilities to circumvent this ambiguity, it is important to understand how people are reading networks and how they interpret the visual elements in the setting of visual network comparison.
The project should analyze the process of network reading and the interpretation of the visual elements. It includes the development of network comparison visualizations with variable encodings.
The students should conduct and evaluate systematic studies in order to dissolve this unfavorable ambiguity. The results should be published at an international conference or in a journal.
Spring Term Spring Term (1. week of April – 3. wk of June) Summer Summer (1. wk June – 3. wk August)
%fall_term% Fall Term (1. wk of October – 3. wk of December)
General Project expiration date:
Pre-requisites or requirements for the project (lab skills, computer languages, etc.):
o Experience in the field of information visualization (network visualization)
o Interest in cognitive psychology
o Experience in conducting user studies
o Knowledge/experience in statistical evaluation of user studies
Recommended literature and preparation:
Graph Visualization Techniques:
1. Gleicher, M., Albers, D., Walker, R., Jusufi, I., Hansen, C. D., and Roberts, J. C.: Visual comparison for information visualization, Information Visualization 10, 4 (Oct. 2011), pp. 289-309, 2011
2. von Landesberger, T., Kuijper, A., Schreck, T., Kohlhammer, J., van Wijk, J.J., Fekete, J.-D. & Fellner, D.W.: Visual Analysis of Large Graphs: State-of-the-art and Future Challenges, Computer Graphics Forum, Vol. 30, No. 6, pp 1719–1749, Sep., 2011
Perception and Cognition in Visualization:
1. Helen C. Purchase: Experimental Human-Computer Interaction: A Practical Guide with Visual Examples, Cambridge University Press, 2012
2. Dix, A., Pohl, M., Ellis, G.: Perception and Cognitive Aspects. In: D. Keim, J. Kohlhammer, G. Ellis, F. Mansmann: Mastering the Information Age: Solving Problems with Visual Analytics, 2010
3. Weidong, H., Hong, S.-H., and Eades, P.. 2006. “How people read sociograms: a questionnaire study.” In Proceedings of the 2006 Asia-Pacific Symposium on Information Visualisation – Volume 60, 199-206.
4. Pohl, M., Smuc, M., and Mayr, E.:
The User Puzzle – Explaining the Interaction with Visual Analytics Systems; Trans. Visualization & Computer Graphics, 18 (2012), 12; p. 2908-2916
5. Körner, C., Concepts and misconceptions in comprehension of hierarchical graphs. Learning and Instruction 15 (2005), pp.281-296, 2005
Credits (ECTS) for the research project:
15 ECTS (Minimum 12 ECTS, preferably 18 ECTS)