AI★★★★arXiv · 2026-07-09
Dimensionality Reduction Meets Network Science: Sensemaking on UMAP's kNN Graph
UMAP is a widely used dimensionality reduction algorithm, but its typical workflow focuses on the lower-dimensional embedding, largely overlooking the rich kNN graph that UMAP constructs internally. This graph encodes the data manifold in its original high-dimensional space. Researchers demonstrate the untapped potential of this internal representation by applying standard graph algorithms to enhance data sensemaking.
📌 Key points
- PageRank identifies representative data points
- k-core decomposition reveals dense regions in the data
- UMAP's kNN graph provides a more nuanced understanding of the data
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