In temporal ( 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 such as below-average performance on tasks as well as loss of precision and difficulties in selecting timeslice intervals. 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 node trajectories and their movement over time, visualizes node "aging", and provides guidance to support users during exploration by indicating interesting time intervals ("when") and network elements ("where") are located for a detail-oriented investigation. This combined focus helps to gain deeper insights into the temporal network's underlying behavior. We assess the utility and efficacy of our approach through two case studies and qualitative expert evaluation. The results demonstrate how TimeLighting supports identifying temporal patterns, extracting insights from nodes with high activity, and guiding the exploration and analysis process.

翻译：在时序（基于事件的）网络中，时间是一个连续轴，每个节点和边都具有实数值的时间坐标。为此类图计算布局意味着将节点轨迹和边曲面随时间嵌入到二维+时间（2D+t）空间中，即时空立方体。目前，这些时空立方体布局通过动画或按固定间隔切片进行可视化。然而，这两种技术都存在任务表现低于平均水平、精度损失以及时间切片间隔选择困难等问题。本文提出TimeLighting，一种新颖的可视分析方法来可视化与探索嵌入时空立方体中的时序图。我们的交互式方法高亮显示节点轨迹及其随时间移动，可视化节点“老化”，并通过指示有趣的时间间隔（“何时”）和网络元素位置（“何处”）来引导用户进行探索，支持面向细节的调查研究。这种组合聚焦有助于更深入地理解时序网络的潜在行为。我们通过两个案例研究和定性专家评估来验证所提方法的实用性与有效性。结果表明，TimeLighting能够有效支持识别时序模式、从高活跃度节点中提取洞察，并引导探索与分析过程。