The enormous growth in the use of electronic devices and systems in the past decades has led to an exponential increase in digital forms of communication. Simultaneously, the abundance of this digital communication and corresponding datasets has increased interest in how such communication can be analyzed in a wide variety of different domains, ranging from social sciences and digital humanities to engineering and business.
The connectivity information of communication is often shown in the form of graphs (node-link diagrams), matrices, or in a hybrid format. However, there is no best solution. Depending on the specific requirements of the domain and the specific tasks, some visualizations might perform better than others. In this work, we systematically and quantitatively analyze the performance of existing state-of-the-art solutions.
- Which tasks exist in the analysis of multivariate node-link diagrams?
- Which visualization is best suited for specific tasks?
- Are there specialized solutions for specific tasks, and how do they perform?
- Reimplementation of existing visualization approaches for multivariate node-link diagrams
- Develop tasks for node search, edge search, and path tracing.
- Quantitative evaluation for these tasks.
- Summarizing and visualizing the results from the above analysis steps interactively.
- Work on cutting-edge research that has important real-life implications.
- Ability to align topics from seminar, project, and thesis and the ability to continue work in this field with more specialized topics for a later thesis.
- Highly motivated
- Excellent programming skills in Python (FastAPI) / D3 / Visualization or comparable
- Experience in network visualization beneficial, but not required
It is possible to work only on a specific sub-aspect of the proposed problems and tasks. Feel free to discuss your preferences with us!
- Scope: Bachelor / Master
- Project / Thesis Duration (Bachelor): 3 months + 3 months
- Project / Thesis Duration (Master): 3 months + 6 months
- Start: At each project/thesis registration slot.
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- Carolina Nobre, Marc Streit, Miriah Meyer, Alexander Lex: The State of the Art in Visualizing Multivariate Networks, Computer Graphics Forum (EuroVis), 38: 807-832, doi:10.1111/cgf.13728, 2019.
- Carolina Nobre, Dylan Wootton, Lane Harrison, and Alexander Lex. 2020. Evaluating Multivariate Network Visualization Techniques Using a Validated Design and Crowdsourcing Approach. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (CHI '20). Association for Computing Machinery, New York, NY, USA, 1–12. DOI: doi.org/10.1145/3313831.3376381