论文标题
DG2PIX：基于像素的动态图的视觉分析
dg2pix: Pixel-Based Visual Analysis of Dynamic Graphs
论文作者
论文摘要
由于潜在的大规模和高维数据，展示长序列动态图仍然具有挑战性。我们提出了一种基于像素的新型可视化技术DG2PIX，以在大尺度图的长序列中视觉探索时间和结构特性。该方法由三个主要步骤组成：（1）时间维度的多尺度建模； （2）无监督的图形嵌入，以学习动态图数据的低维表示； （3）基于交互式像素的可视化，以同时探索不同时间聚集量表的不断发展的数据。 DG2PIX提供了动态图的可扩展概述，支持对高维图数据的长序列的探索，并可以识别和比较相似的时间状态。我们显示了该技术对合成和现实世界数据集的适用性，证明可以随时间识别和解释动态图中的时间模式。 DG2PIX在高细节端和低细节端的矩阵表示之间贡献了适当的中间表示。
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.
