Visualizing High-Dimensional Graph Embeddings via Informed Multi-View Projections
For graph visualization researchers and practitioners, this work provides a new method to reveal structural patterns hidden in conventional 2D visualizations.
The paper proposes embedding graphs in high-dimensional space and searching for informative 2D viewpoints that optimize aesthetic metrics like edge crossings and angular resolution, enabled by a differentiable surrogate for edge crossings. Results show these viewpoints consistently outperform standard 2D layouts and can surpass methods explicitly designed to optimize these metrics.
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.