Song Sicheng, Zhang Yipeng, Lin Yanna, Qu Huamin, Wang Changbo, Li Chenhui
IEEE Trans Vis Comput Graph. 2025 Sep;31(9):5975-5989. doi: 10.1109/TVCG.2024.3485701.
Incorporating automatic style extraction and transfer from existing well-designed graph visualizations can significantly alleviate the designer's workload. There are many types of graph visualizations. In this paper, our work focuses on node-link diagrams. We present a novel approach to streamline the design process of graph visualizations by automatically extracting visual styles from well-designed examples and applying them to other graphs. Our formative study identifies the key styles that designers consider when crafting visualizations, categorizing them into global and local styles. Leveraging deep learning techniques such as saliency detection models and multi-label classification models, we develop end-to-end pipelines for extracting both global and local styles. Global styles focus on aspects such as color scheme and layout, while local styles are concerned with the finer details of node and edge representations. Through a user study and evaluation experiment, we demonstrate the efficacy and time-saving benefits of our method, highlighting its potential to enhance the graph visualization design process.
从现有的精心设计的图形可视化中融入自动样式提取和转换，可以显著减轻设计师的工作量。图形可视化有多种类型。在本文中，我们的工作重点是节点链接图。我们提出了一种新颖的方法，通过从精心设计的示例中自动提取视觉样式并将其应用于其他图形，来简化图形可视化的设计过程。我们的形成性研究确定了设计师在制作可视化时考虑的关键样式，并将其分为全局样式和局部样式。利用显著性检测模型和多标签分类模型等深度学习技术，我们开发了用于提取全局和局部样式的端到端管道。全局样式关注配色方案和布局等方面，而局部样式则关注节点和边表示的更细微细节。通过用户研究和评估实验，我们证明了我们方法的有效性和节省时间的好处，突出了其增强图形可视化设计过程的潜力。
