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Efficient Approximate Nearest Neighbor Search via Hemi-Sphere Centroids Graph

Summary: Analyzes MRNG under cosine similarity, proving greedy search monotonically approaches the query until the true NN and that max out-degree is constant (dataset-size independent), explaining fast search and compact indices. Proposes Hemi-Sphere Centroids Graph (HSCG), an efficient approximate MRNG using hemi-sphere centroids and LSH-based initialization to build cosine-aware graph indices that outperform baselines in search speed and index size. (summarized by gpt-5-mini on Feb 11 2026)

Paper ID
7382
Venue
SIGMOD
Year
2026
Pagerank
4.1945683e-05
Overall Rank
10,073 | 29.93%
DOI
10.1145/3769786

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