A Comprehensive Survey and Experimental Comparison of Graph-Based Approximate Nearest Neighbor Search
Summary: Systematically taxonomizes and benchmarks 13 graph-based ANNS algorithms across 20 real and synthetic datasets using a unified, fine-grained pipeline. Reveals component-level tradeoffs, practitioner guidelines, and an optimized method that surpasses prior state of the art. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Mengzhao Wang (Hangzhou Dianzi University)
- 2. Xiaoliang Xu (Hangzhou Dianzi University)
- 3. Qiang Yue (Hangzhou Dianzi University)
- 4. Yuxiang Wang (Hangzhou Dianzi University)
BibTeX Citation
@article{wang_vldb21,
title = {{A Comprehensive Survey and Experimental Comparison of Graph-Based Approximate Nearest Neighbor Search}},
author = {Wang, Mengzhao and Xu, Xiaoliang and Yue, Qiang and Wang, Yuxiang},
journal = {PVLDB},
series = {{VLDB} '21},
volume = {14},
number = {11},
pages = {1964--1978},
doi = {10.14778/3476249.3476255},
url = {https://doi.org/10.14778/3476249.3476255},
year = {2021}
}
Incoming Citations (Sorted by Pagerank)
Showing 25 of 75 citing papers.
Outgoing Citations (Sorted by Pagerank)
Showing 9 of 9 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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