dynamic networks

V. Filipov, Ceneda, D., Archambault, D., and Arleo, A., “TimeLighting: Guided Exploration of 2D Temporal Network Projections”, IEEE Transactions on Visualization and Computer Graphics, p. 13, 2024.
Advisor
Co-Advisor
Abstract

Analyzing large, dynamic network data, such as the Database of Modern Exhibitions, presents significant challenges due to the dataset's scale and complexity, as it tracks a decade of European art exhibitions and encompasses thousands of artists with evolving relationships. Centrality measures help address this complexity by using algorithms to quantify the importance of each node. However, an important next step is to explore how these calculated importance metrics can be transformed into meaningful visual representations to extract insights and identify key actors.To achieve this, we conducted a state-of-the-art literature review to investigate how centrality measures are applied in data visualization, which informed the development of dome-insights, a visual analytics tool that integrates centrality measures into both its visualization and interaction design. Dome-insights serves as a prototype to demonstrate how incorporating centrality measures can enhance the exploration of large, complex networks, facilitating insight discovery and identification of key actors.The tool was assessed by art historians and delivered positive results in both quantitative and qualitative evaluations, demonstrating its ability to uncover meaningful insights. More broadly, the findings highlight the potential of centrality measures in dynamic network analysis, underscoring their role in enhancing visual analytics for complex network data.

Year of Publication
2024
Secondary Title
Institute of Visual Computing and Human-Centered Technology
Paper
Number of Pages
104
reposiTUm Handle
20.500.12708/205281
Publisher
TU Wien
Place Published
Vienna
DOI
10.34726/hss.2024.124190
L. Rauchenberger, “Dynamic Network Analysis with Centrality Measures”, Institute of Visual Computing and Human-Centered Technology. TU Wien, Vienna, p. 104, 2024.
Master Thesis
AC17385406
N. -M. Herl and Filipov, V., “AdMaTilE: Visualizing Event-Based Adjacency Matrices in a Multiple-Coordinated-Views System”, in 32nd International Symposium on Graph Drawing and Network Visualization (GD 2024), 2024, vol. 320, pp. 46:1-46:3.
V. Filipov, “"Networks in Time and Space, Visual Analytics of Dynamic Network Representations"”, TU Wien, Vienna, 2024.
F. Windhager, Amor-Amorós, A., Smuc, M., Federico, P., Zenk, L., and Miksch, S., “A concept for the exploratory visualization of patent network dynamics”, in Proceedings of the 6th International Conference on Information Visualization Theory and Applications, 2015.
P. Federico, Aigner, W., Miksch, S., Windhager, F., and Zenk, L., “A Visual Analytics Approach to Dynamic Social Networks”, in Proceedings of the 11th International Conference on Knowledge Management and Knowledge Technologies (i-KNOW), Special Track on Theory and Applications of Visual Analytics (TAVA), 2011, pp. 47:1–47:8.