The DOI matches its Crossref record; 81 of the 81 references it cites were found in a registry or an index.
A comprehensive survey and experimental comparison of graph-based approximate nearest neighbor search
Open accessAbstract
Approximate nearest neighbor search (ANNS) constitutes an important operation in a multitude of applications, including recommendation systems, information retrieval, and pattern recognition. In the past decade, graph-based ANNS algorithms have been the leading paradigm in this domain, with dozens of graph-based ANNS algorithms proposed. Such algorithms aim to provide effective, efficient solutions for retrieving the nearest neighbors for a given query. Nevertheless, these efforts focus on developing and optimizing algorithms with different approaches, so there is a real need for a comprehensive survey about the approaches' relative performance, strengths, and pitfalls. Thus here we provide a thorough comparative analysis and experimental evaluation of 13 representative graph-based ANNS algorithms via a new taxonomy and fine-grained pipeline. We compared each algorithm in a uniform test environment on eight real-world datasets and 12 synthetic datasets with varying sizes and characteristics. Our study yields novel discoveries, offerings several useful principles to improve algorithms, thus designing an optimized method that outperforms the state-of-the-art algorithms. This effort also helped us pinpoint algorithms' working portions, along with rule-of-thumb recommendations about promising research directions and suitable algorithms for practitioners in different fields.
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Cites
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Cited by
Douze, M., Guzhva, A., Deng, C., Johnson, J., Szilvasy, G., Mazaré, P.-E., Lomeli, M., Hosseini, L., & Jégou, H. (2026) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2025.3618474 · Cited by 478
Survey of Vector Database Management Systems
Pan, J. J., Wang, J., & Li, G. (2023) · arXiv · doi:10.48550/arxiv.2310.14021 · Cited by 184
Accelerating Retrieval-Augmented Generation
Quinn, D., Nouri, M., Patel, N., Salihu, J., Salemi, A., Lee, S., Zamani, H., & Alian, M. (2025) · Proceedings of the 30th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 1 · doi:10.1145/3669940.3707264 · Cited by 48
GaussDB-Vector: A Large-Scale Persistent Real-Time Vector Database for LLM Applications
Sun, J., Li, G., Pan, J., Wang, J., Xie, Y., Liu, R., & Nie, W. (2025) · Proceedings of the VLDB Endowment · doi:10.14778/3750601.3750619 · Cited by 13
P-HNSW: Crash-Consistent HNSW for Vector Databases on Persistent Memory
Lee, H., Park, T., Na, Y., & Kim, W.-H. (2025) · Applied Sciences · doi:10.3390/app151910554 · Cited by 1
Metadata
- Authors
- Wang, M., Xu, X., Yue, Q., & Wang, Y.
- Venue
- Proceedings of the VLDB Endowment
- Published
- 2021-07
- Volume
- 14
- Issue
- 11
- Pages
- 1964-1978
- ISSN
- 2150-8097 (ISSN-L)
- OpenAlex
- W3128793550
- Cited by
- 264
- References
- 81
- FWCI
- 8.73
- Type
- journal article
- Language
- en
- Open access
- green
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