arXiv:2606.31119v1 Announce Type: new 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
The proliferation of high-dimensional data, particularly in complex graph structures, necessitates improved visualization techniques for human interpretability and decision-making.
This development enhances the ability to extract insights from complex AI models and large datasets, a critical bottleneck in advanced AI and scientific research.
Current visualizations of high-dimensional graph embeddings are suboptimal; new methods promise more informative and readable representations, potentially accelerating human understanding of complex AI outputs.
- · AI researchers
- · Data scientists
- · Graph analytics platforms
- · UX/UI designers for complex systems
- · Methods relying solely on standard 2D graph visualizations
Improved visualization tools for high-dimensional graph data.
Faster development and debugging cycles for complex AI models due to better interpretability.
Enhanced human-AI collaboration and trust as AI systems become more 'explainable' through clearer visualizations.
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Read at arXiv cs.LG