Watkin, L., Archambault, D., & Telea, A. (2026). Shapdbm: Exploring decision boundary maps in shapley space. https://doi.org/10.2312/EVS.20261004
Tag: Daniel
Bundling-Aware Graph Drawing Revisited
Wallinger, M., Piselli, T., Tappini, A., Archambault, D., Liotta, G., & Nöllenburg, M. (2025). Bundling-aware graph drawing revisited. IEEE Transactions on Visualization and Computer Graphics, 31(12), 10828–10839. https://doi.org/10.1109/TVCG.2025.3616583
Layers of Doubt: Typology of Temporal Uncertainty in Dynamic Diffusion Networks
Baumgartl, T., Filipov, V., Rajendran, S., Miksch, S., Archambault, D., Arleo, A., & Von Landesberger, T. (2025). Layers of doubt: Typology of temporal uncertainty in dynamic diffusion networks. 2025 IEEE Workshop on Uncertainty Visualization: Unraveling Relationships of Uncertainty, AI, and Decision-Making, 53–57. https://doi.org/10.1109/UncertaintyVisualization68947.2025.00012 🏆 Best Paper Honourable Mention at VIS 2025 Uncertainty Workshop
Embarrassingly Agile — Data Visualization Methodology in Emergency Responses
Kozlikova, B., Archambault, D., Dreesman, J., Kerren, A., Lucini, B., & Turkay, C. (2025). Embarrassingly agile—Data visualization methodology in emergency responses. IEEE Computer Graphics and Applications, 45(5), 138–146. https://doi.org/10.1109/MCG.2025.3595342
Financial Forecasting in Consumer Cyclicals with Economic Indicators and Tokenization
Krawczyk, K., Tam, G. K. L., & Archambault, D. (2025). Financial forecasting in consumer cyclicals with economic indicators and tokenization. International Journal of Computer Theory and Engineering, 17(4), 179–188. https://doi.org/10.7763/IJCTE.2025.V17.1380
Are large screens effective at supporting the analysis of delay visualizations?
Almajyul, A., Archambault, D., & Forshaw, M. (2025). Are large screens effective at supporting the analysis of delay visualizations? 2025 29th International Conference Information Visualisation (IV), 13–18. https://doi.org/10.1109/IV68685.2025.00015
Methods for Interventions using Networks to Improve Health: a narrative synthesis of methodological research on network data collection, visualisation and intervention
Riddell, J., Letina, S., Skivington, K., Archambault, D., Wells, V., Long, E., Hunter, R., & McCann, M. (2026). Methods for interventions using networks to improve health: A narrative synthesis of methodological research on network data collection, visualisation and intervention. Social Networks, 84, 202–219. https://doi.org/10.1016/j.socnet.2025.10.003
Third Place at GD Contest 2025
George, Luke, Alma and Daniel, with partners from the University of Cologne, designed and created a visualisation for submission to The 33rd International Symposium on Graph Drawing and Network Visualization Creative Topic challenge. Of twelve submissions, they achieved third place!
The challenge was to create a visualisation for the German science fiction show Dark, visualising its complex non-linear storyline containing frequent time-travel, multiple realities and a large cast of interacting characters. The final visualisation was projected onto the Norrköping Decision Arena‘s 360° degree screen .
For an interactable demo, click here.
Navigating High-Dimensional Backstage: A Guide for Exploring Literature for the Reliable Use of Dimensionality Reduction
Jeon, H., Lee, H., Kuo, Y.-H., Yang, T., Archambault, D., Ko, S., Fujiwara, T., Ma, K.-L., & Seo, J. (2025). Navigating high-dimensional backstage: A guide for exploring literature for the reliable use of dimensionality reduction. EuroVis 2025 – Short Papers. https://doi.org/10.2312/EVS.20251087
Unveiling High-dimensional Backstage: A Survey for Reliable Visual Analytics with Dimensionality Reduction
Jeon, H., Lee, H., Kuo, Y.-H., Yang, T., Archambault, D., Ko, S., Fujiwara, T., Ma, K.-L., & Seo, J. (2025). Unveiling high-dimensional backstage: A survey for reliable visual analytics with dimensionality reduction. Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, 1–24. https://doi.org/10.1145/3706598.3713551