Project: Investigating Guidance Techniques for Network Visualization
Description
Networks—both static and temporal—are complex data structures that pose significant challenges for effective visualization and user interpretation. Guidance techniques in visual analytics aim to support users by providing automated or semi-automated assistance during exploration, helping to reduce cognitive load and improve insight discovery.
This project will begin by investigating the current state of guidance methods applied to network visualization. Drawing on foundational work in visual analytics guidance [1, 2] and recent advances specific to temporal graphs [3], the project will review and categorize the types of guidance that have been utilized for static and dynamic networks. Building on this analysis, the project will formalize a taxonomy of guidance strategies tailored to network visualization.
Finally, the project will implement a prototype visualization tsystem incorporating several selected guidance techniques to support user interaction with both static and temporal networks. The prototype will demonstrate how guidance can facilitate the exploration and understanding of complex network structures over time.
[1] Ceneda, D., Gschwandtner, T., May, T., Miksch, S., Schulz, H. J., Streit, M., & Tominski, C. (2016). Characterizing guidance in visual analytics. IEEE transactions on visualization and computer graphics, 23(1), 111-120.
[2] Ceneda, D., Gschwandtner, T., & Miksch, S. (2019, June). A review of guidance approaches in visual data analysis: A multifocal perspective. In Computer Graphics Forum (Vol. 38, No. 3, pp. 861-879).
[3] Filipov, V., Ceneda, D., Archambault, D., & Arleo, A. (2024). TimeLighting: Guided Exploration of 2D Temporal Network Projections. IEEE Transactions on Visualization and Computer Graphics.
Details
- Supervisor
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Hrim Mehta
- Secondary supervisor
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Alessio Arleo
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