Abstract
In temporal (or event-based) networks, time is a continuous axis, with real-valued time coordinates for each node and edge. Computing a layout for such graphs means embedding the node trajectories and edge surfaces over time in a \(2D + t\) space, known as the space-time cube. Currently, these space-time cube layouts are visualized through animation or by slicing the cube at regular intervals. However, both techniques present problems ranging from sub-par performance on some tasks to loss of precision. In this paper, we present TimeLighting, a novel visual analytics approach to visualize and explore temporal graphs embedded in the space-time cube. Our interactive approach highlights the node trajectories and their mobility over time, visualizes node “aging”, and provides guidance to support users during exploration. We evaluate our approach through two case studies, showing the system’s efficacy in identifying temporal patterns and the role of the guidance features in the exploration process.
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For the purpose of open access, the author has applied a Creative Commons Attribution (CC-BY) license to any Author Accepted Manuscript version arising from this submission. This work was conducted within the projects WWTF grant [10.47379/ICT19047], FFG grant DoRIAH [#880883], and FWF grant ArtVis [P35767].
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Filipov, V., Ceneda, D., Archambault, D., Arleo, A. (2023). TimeLighting: Guidance-Enhanced Exploration of 2D Projections of Temporal Graphs. In: Bekos, M.A., Chimani, M. (eds) Graph Drawing and Network Visualization. GD 2023. Lecture Notes in Computer Science, vol 14465. Springer, Cham. https://doi.org/10.1007/978-3-031-49272-3_16
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