Visualizing High-Dimensional Graph Embeddings via Informed Multi-View Projections
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Computer Science > Machine Learning
Title:Visualizing High-Dimensional Graph Embeddings via Informed Multi-View Projections
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.
| Comments: | 18 pages, 13 figures |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2606.31119 [cs.LG] |
| (or arXiv:2606.31119v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.31119
arXiv-issued DOI via DataCite (pending registration)
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