TimeLighting: Guided Exploration of 2D Temporal Network Projections

Velitchko Filipov;Davide Ceneda;Daniel Archambault;Alessio Arleo
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Abstract

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 article, 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.
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时间照明:2D时间网络投影的引导探索
在时间(基于事件的)网络中,时间是一个连续的轴,每个节点和边缘都有实值的时间坐标。计算这种图的布局意味着将节点轨迹和边缘表面随时间嵌入到2D + t空间中,称为时空立方体。目前,这些时空立方体布局是通过动画或定期切片立方体来可视化的。然而,这两种技术都存在一些问题,例如任务的性能低于平均水平,以及精度的损失和选择时间片间隔的困难。在本文中,我们介绍了timellighting,一种新颖的可视化分析方法,用于可视化和探索嵌入在时空立方体中的时间图。我们的交互式方法突出了节点轨迹及其随时间的移动,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(“何处”),为面向细节的调查提供指导,以支持用户在探索过程中。这种综合关注有助于更深入地了解时间网络的潜在行为。我们通过两个案例研究和定性专家评估来评估我们方法的效用和有效性。结果展示了timellighting如何支持识别时间模式,从高活动节点中提取见解,并指导探索和分析过程。
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