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

UMAP's internal kNN graph offers a superior topological representation for data exploration than 2D projections. Analyzing this high-dimensional graph directly using network science algorithms like PageRank and k-core decomposition reveals data manifolds without projection distortion. This approach gives machine learning developers a more faithful way to identify representative samples and understand complex high-dimensional datasets.

Opening Kapyn…