Dimensionality Reduction Meets Network Science: Sensemaking on UMAP's kNN Graph

Duen Horng Chau, Donghao Ren, Fred Hohman, Dominik Moritz|July 9, 2026arXiv

Key Takeaway

The kNN graph UMAP builds internally preserves high-dimensional structure better than the 2D visualization; applying network analysis to it gives you cleaner insights into data organization than the embedding alone.

Summary

UMAP is popular for visualizing high-dimensional data, but researchers typically ignore its internal k-nearest-neighbor graph.

evaluation data

Key Terms

dimensionality-reduction knn-graph manifold k-core-decomposition