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Wolverine: Highly Efficient Monotonic Search Path Repair for Graph-based ANN Index Updates

Summary: Wolverine: a monotonic search-path repair framework for dynamic graph-based ANN indices that fixes broken monotonic paths by adding in-edges to out-neighbors of deleted nodes to preserve connectivity and recall. Wolverine+ (2‑hop restriction) and Wolverine++ (quality-driven candidate selection) speed deletions up to 11× and maintain steadier recall vs. prior dynamic ANN methods across 9 real datasets. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
13877
Venue
VLDB
Year
2025
Pagerank
4.1945683e-05
Overall Rank
10,602 | 26.25%
DOI
10.14778/3748389.3734860

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