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Dimensionality Reduction Meets Network Science: Sensemaking on UMAP's kNN Graph
While UMAP is widely used for exploring high dimensional data, typical workflows focus on its lower dimensional embedding, largely overlooking the rich k nearest neighbor (kNN) graph that UMAP constructs internally. This graph encodes the data manifold in its original high dimensional space, before the distortion that UMAP's 2D projection introduces. We demonstrate the untapped potential of this internal represent...