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 representation, showing how standard graph algorithms applied to this graph enhance data sensemaking: (1) PageRank identifies representative data points, (2) k-core decomposition reveals dense
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- LinkedLinked via arxiv author · 85%Duen Horng Chau →
“Dimensionality Reduction Meets Network Science: Sensemaking on UMAP's kNN Graph”
- LinkedLinked via arxiv author · 85%Donghao Ren →
“Dimensionality Reduction Meets Network Science: Sensemaking on UMAP's kNN Graph”
- LinkedLinked via arxiv author · 85%Fred Hohman →
“Dimensionality Reduction Meets Network Science: Sensemaking on UMAP's kNN Graph”
- LinkedLinked via arxiv author · 85%Dominik Moritz →
“Dimensionality Reduction Meets Network Science: Sensemaking on UMAP's kNN Graph”
- FuzzySimilar title/name (fuzzy) · 59%tirth8205/code-review-graph →
“Fuzzy title match (0.73): “Dimensionality Reduction Meets Network Science: Sensemaking ” ≈ “tirth8205/code-review-graph””