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

Researchers demonstrate that the k-nearest-neighbor (kNN) graph constructed internally by UMAP can be leveraged with standard graph algorithms to enhance understanding of high-dimensional data, revealing representative points, dense cores, and tight-knit neighborhoods. Evaluations on MNIST and Fashion MNIST datasets show these graph-based methods are competitive with specialized techniques like k-medoids and HDBSCAN.