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UMAP kNN Graph Analysis

1 post · Jul 2026

What this trail covers

UMAP's internal kNN graph is being leveraged for enhanced data sensemaking. Standard graph algorithms like PageRank, k-core decomposition, and clustering coefficient are applied to this graph to identify representative data points, reveal dense core regions, and detect tight-knit neighborhoods of similar data points. These methods offer practical and competitive alternatives to existing sensemaking techniques.

All Apple trails
  1. Dimensionality Reduction Meets Network Science: Sensemaking on UMAP’s kNN GraphThis post introduces the application of standard graph algorithms (PageRank, k-core decomposition, clustering coefficient) to UMAP's internal kNN graph for data sensemaking. It demonstrates how these algorithms can identify representative data points, reveal dense core regions, and detect similar data neighborhoods, providing quantitative and qualitative evaluations on MNIST and Fashion MNIST datasets.