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

What this post added

Introduces the application of PageRank for identifying representative data points, k-core decomposition for revealing dense core regions versus sparse periphery, and clustering coefficient for detecting tight-knit neighborhoods within UMAP's internal kNN graph. Evaluates these methods on MNIST and Fashion MNIST datasets.