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)
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Authors
- 1. Dawei Liu
- 2. Bolong Zheng
- 3. Ziyang Yue
- 4. Fuhao Ruan
- 5. Xiaofang Zhou
- 6. Christian S. Jensen
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