DBScholar

Back to papers

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
14065
Venue
VLDB
Year
2025
Pagerank
5.3016261e-05
Overall Rank
9,236 | 36.64%
DOI
10.14778/3734839.3734860

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{liu_vldb25,
        title = {{Wolverine: Highly Efficient Monotonic Search Path Repair for Graph-based ANN Index Updates}},
        author = {Liu, Dawei and Zheng, Bolong and Yue, Ziyang and Ruan, Fuhao and Zhou, Xiaofang and Jensen, Christian S.},
        journal = {PVLDB},
        series = {{VLDB} '25},
        volume = {18},
        number = {7},
        pages = {2268--2280},
        doi = {10.14778/3734839.3734860},
        url = {https://doi.org/10.14778/3734839.3734860},
        year = {2025}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Previous Page 1 / 1 Next

Outgoing Citations (Sorted by Pagerank)

Showing 11 of 11 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Previous Page 1 / 1 Next

Semantically Similar Papers