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arXiv · 2606.31119

Visualizing High-Dimensional Graph Embeddings via Informed Multi-View Projections

Ya Ji·Xuefeng Li·Timo Brand·Jacob Miller·Peng Zhang·Stephen Kobourov·Yifan Hu
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Abstract

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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Ya Ji, Xuefeng Li, Timo Brand, Jacob Miller, Peng Zhang, Stephen Kobourov, Yifan Hu. 2026-06-30. Visualizing High-Dimensional Graph Embeddings via Informed Multi-View Projections. https://arxiv.org/abs/2606.31119

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