Presenting long sequences of dynamic graphs remains challenging due to the underlying large-scale and high-dimensional data. We propose dg2pix, a novel pixel-based visualization technique, to visually explore temporal and structural properties in long sequences of large-scale graphs. The approach consists of three main steps: (1) the multiscale modeling of the temporal dimension; (2) unsupervised graph embeddings to learn low-dimensional representations of the dynamic graph data; and (3) an interactive pixel-based visualization to simultaneously explore the evolving data at different temporal aggregation scales. dg2pix provides a scalable overview of a dynamic graph, supports the exploration of long sequences of high-dimensional graph data, and enables the identification and comparison of similar temporal states. We show the applicability of the technique to synthetic and real-world datasets, demonstrating that temporal patterns in dynamic graphs can be identified and interpreted over time. dg2pix contributes a suitable intermediate representation between node-link diagrams at the high detail end and matrix representations on the low detail end.

中文翻译：

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dg2pix：基于像素的动态图形可视化分析

由于潜在的大规模和高维数据，呈现长序列的动态图仍然具有挑战性。我们提出了 dg2pix，一种新颖的基于像素的可视化技术，可以直观地探索大型图形长序列中的时间和结构特性。该方法包括三个主要步骤：（1）时间维度的多尺度建模；(2) 无监督图嵌入学习动态图数据的低维表示；(3) 一种基于像素的交互式可视化，可同时探索不同时间聚合尺度上的演化数据。dg2pix 提供动态图的可扩展概览，支持对长序列高维图数据的探索，并能够识别和比较相似的时间状态。我们展示了该技术对合成和现实世界数据集的适用性，证明动态图中的时间模式可以随着时间的推移被识别和解释。dg2pix 在高细节端的节点链接图和低细节端的矩阵表示之间提供了合适的中间表示。