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Dimensionality Reduction Meets Network Science: Sensemaking on UMAP's kNN Graph
The authors explore the potential of using the internal k-nearest-neighbor (kNN) graph constructed by UMAP, a popular tool for exploring high-dimensional data. They demonstrate that standard graph algorithms like…
Dimensionality Reduction Meets Network Science: Sensemaking on UMAP's kNN Graph
The authors of the paper 'Dimensionality Reduction Meets Network Science: Sensemaking on UMAP's kNN Graph', published in 2026, discuss a new approach to utilizing the internal k-nearest-neighbor (kNN) graph constructed…
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