通过可微投影找到更清晰的图可视化视角，揭示传统方法隐藏的结构。
Visualizing High-Dimensional Graph Embeddings via Informed Multi-View Projections
- 用可微近似替代边交叉计算，高效搜索最优2D投影视角。
- 在多个数据集上优于标准布局和专门优化的算法。
- 适合需要发现复杂图结构的科研人员与数据分析师。
图常以二维形式可视化，人类能直观理解空间关系，但此类布局往往扭曲高维结构。本文提出将图嵌入高维空间，并通过新型可微边交叉近似，搜索能优化美观性与可读性指标（如边交叉数、角度分辨率）的2D视角。数值实验表明，这些视角在多个数据集上持续优于标准2D布局，甚至超越专为优化这些指标设计的方法。我们进一步开发了DataFly交互系统，支持无缝导航多个候选视角。可用性研究表明，该方法揭示了传统2D可视化中隐藏的结构模式。
原文摘要 · Abstract (English)
Graphs are commonly visualized in 2D, where humans readily interpret spatial relationships, yet such layouts often distort higher-dimensional structure. We propose to embed graphs in high-dimensional space and search for informative 2D viewpoints that optimize aesthetic and readability metrics (e.g., edge crossings and angular resolution), enabled by a novel differentiable surrogate for edge crossings. Numerical experiments show that these viewpoints consistently outperform standard 2D layouts, and can even surpass methods explicitly designed to optimize these metrics. We further introduce DataFly, an interactive system for exploring multiple candidate viewpoints through seamless navigation. A usability study demonstrates that our approach reveals structural patterns that remain hidden in conventional 2D visualizations.
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