Fetching the paper…
Reading the bibliography…
Given a vector dataset $\mathcal{X}$ and a query vector $\vec{x}_q$, graph-based Approximate Nearest Neighbor Search (ANNS) aims to build a graph index $G$ and approximately return vectors with minimum distances to $\vec{x}_q$ by searching over $G$.
T. Cover and P. Hart, “Nearest neighbor pattern classification,” IEEE transactions on information theory , vol. 13, no. 1, pp. 21–27, 1967
1967
Earlier work this paper cites.
G. Karypis, V. Kumar, and S. Comput, “A fast and high quality multilevel scheme for partitioning irregular graphs,” SIAM Journal on Scientific Computing , vol. 20, 02 1970
1970
Earlier work this paper cites.
M. Fiedler, “Algebraic connectivity of graphs,” Czechoslovak Mathematical Journal , vol. 23, pp. 298–305, 1973. [Online]. Available: https://api.semanticscholar.org/CorpusID:117770486
1973
Earlier work this paper cites.
D. Dearholt, N. Gonzales, and G. Kurup, “Monotonic search networks for computer vision databases,” vol. 2, 02 1988, pp. 548–553
1988
Earlier work this paper cites.
S. Arya and D. M. Mount, “Approximate nearest neighbor queries in fixed dimensions.” in SODA , vol. 93, 1993, pp. 271–280
1993
Earlier work this paper cites.
M. Flickner, H. Sawhney, W. Niblack, J. Ashley, Q. Huang, B. Dom, M. Gorkani, J. Hafner, D. Lee, D. Petkovic et al. , “Query by image and video content: The qbic system,” computer , vol. 28, no. 9, pp. 23–32, 1995
1995
Earlier work this paper cites.
P. Indyk and R. Motwani, “Approximate nearest neighbors: towards removing theff curse of dimensionality,” in Proceedings of the thirtieth annual ACM symposium on Theory of computing , 1998, pp. 604–613
1998
Earlier work this paper cites.
A. Gionis, P. Indyk, R. Motwani et al. , “Similarity search in high dimensions via hashing,” in Vldb , vol. 99, no. 6, 1999, pp. 518–529
1999
Earlier work this paper cites.
B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, “Item-based collaborative filtering recommendation algorithms,” in Proceedings of the 10th international conference on World Wide Web , 2001, pp. 285–295
2001
Earlier work this paper cites.
R. Paredes and E. Chávez, “Using the k-nearest neighbor graph for proximity searching in metric spaces,” in String Processing and Information Retrieval: 12th International Conference, SPIRE 2005, Buenos Aires, Argentina, November 2-4, 2005. Proceedings 12 . Springer, 2005, pp. 127–138
2005
Earlier work this paper cites.
C. Silpa-Anan and R. Hartley, “Optimised kd-trees for fast image descriptor matching,” in 2008 IEEE Conference on Computer Vision and Pattern Recognition . IEEE, 2008, pp. 1–8
2008
Earlier work this paper cites.
Y. Weiss, A. Torralba, and R. Fergus, “Spectral hashing,” in Advances in Neural Information Processing Systems , D. Koller, D. Schuurmans, Y. Bengio, and L. Bottou, Eds., vol. 21. Curran Associates, Inc., 2008
2008
Earlier work this paper cites.
H. Jegou, M. Douze, and C. Schmid, “Product quantization for nearest neighbor search,” IEEE transactions on pattern analysis and machine intelligence , vol. 33, no. 1, pp. 117–128, 2010
2010
Earlier work this paper cites.
H. Hacid and T. Yoshida, “Neighborhood graphs for indexing and retrieving multi-dimensional data,” Journal of Intelligent Information Systems , vol. 34, pp. 93–111, 2010
2010
Earlier work this paper cites.
Anon. (2010) Datasets for approximate nearest neighbor search. [Online]. Available: http://corpus-texmex.irisa.fr/
2010
Earlier work this paper cites.
M. Babenko and A. Gusakov, “New exact and approximation algorithms for the star packing problem in undirected graphs,” Symposium on Theoretical Aspects of Computer Science (STACS2011) , vol. 9, 03 2011
2011
Earlier work this paper cites.
Song. (2011) Million song dataset benchmarks. [Online]. Available: http://www.ifs.tuwien.ac.at/mir/msd/
2011
Earlier work this paper cites.
H. Jégou, M. Douze, and C. Schmid, “Product quantization for nearest neighbor search,” IEEE transactions on pattern analysis and machine intelligence , vol. 33, pp. 117–28, 01 2011
2011
Earlier work this paper cites.
H. Xu, J. Wang, Z. Li, G. Zeng, S. Li, and N. Yu, “Complementary hashing for approximate nearest neighbor search,” 11 2011, pp. 1631–1638
2011
Earlier work this paper cites.
K. Hajebi, Y. Abbasi-Yadkori, H. Shahbazi, and H. Zhang, “Fast approximate nearest-neighbor search with k-nearest neighbor graph,” in Twenty-Second International Joint Conference on Artificial Intelligence , 2011
2011
Earlier work this paper cites.
I. Stanton and G. Kliot, “Streaming graph partitioning for large distributed graphs,” 09 2012
2012
Earlier work this paper cites.
K. Aoyama, A. Ogawa, T. Hattori, T. Hori, and A. Nakamura, “Graph index based query-by-example search on a large speech data set,” in 2013 IEEE International Conference on Acoustics, Speech and Signal Processing . IEEE, 2013, pp. 8520–8524
2013
Earlier work this paper cites.
T. Ge, K. He, Q. Ke, and J. Sun, “Optimized product quantization for approximate nearest neighbor search,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2013, pp. 2946–2953
2013
Earlier work this paper cites.
V. Bijalwan, V. Kumar, P. Kumari, and J. Pascual, “Knn based machine learning approach for text and document mining,” International Journal of Database Theory and Application , vol. 7, no. 1, pp. 61–70, 2014
2014
Earlier work this paper cites.
C. Xie, L. Yan, W.-J. Li, and Z. Zhang, “Distributed power-law graph computing: Theoretical and empirical analysis,” Advances in Neural Information Processing Systems , vol. 2, pp. 1673–1681, 01 2014
2014
Cited alongside, same era.
C. Tsourakakis, C. Gkantsidis, B. Radunovic, and M. Vojnovic, “Fennel: Streaming graph partitioning for massive scale graphs,” 02 2014, pp. 333–342
2014
Cited alongside, same era.
Y. Kalantidis and Y. Avrithis, “Locally optimized product quantization for approximate nearest neighbor search,” In CVPR , pp. 2321–2328, 01 2014
2014
Cited alongside, same era.
Y. Malkov, A. Ponomarenko, A. Logvinov, and V. Krylov, “Approximate nearest neighbor algorithm based on navigable small world graphs,” Information Systems , vol. 45, pp. 61–68, 2014
2014
Cited alongside, same era.
F. Petroni, L. Querzoni, K. Daudjee, S. Kamali, and G. Iacoboni, “Hdrf: Stream-based partitioning for power-law graphs,” 10 2015, pp. 243–252
M. Zhang and Y. He, “Grip: Multi-store capacity-optimized high-performance nearest neighbor search for vector search engine,” in Proceedings of the 28th ACM International Conference on Information and Knowledge Management , 11 2019, pp. 1673–1682
2019
Later among the works it cites.
L. Gong, H. Wang, M. Ogihara, and J. Xu, “idec: indexable distance estimating codes for approximate nearest neighbor search,” Proceedings of the VLDB Endowment , vol. 13, no. 9, 2020
2020
Later among the works it cites.
M. Aumüller, E. Bernhardsson, and A. Faithfull, “Ann-benchmarks: A benchmarking tool for approximate nearest neighbor algorithms,” Information Systems , vol. 87, p. 101374, 2020
2020
Later among the works it cites.
Z. Pan, L. Wang, Y. Wang, and Y. Liu, “Product quantization with dual codebooks for approximate nearest neighbor search,” Neurocomputing , vol. 401, 03 2020
2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2015
Cited alongside, same era.
P. Jeffrey, S. Richard, and D. M. Christopher. (2015) Glove: Global vectors for word representation. [Online]. Available: http://nlp.stanford.edu/projects/glove/
2015
Cited alongside, same era.
D. A. Adeniyi, Z. Wei, and Y. Yongquan, “Automated web usage data mining and recommendation system using k-nearest neighbor (knn) classification method,” Applied Computing and Informatics , vol. 12, no. 1, pp. 90–108, 2016
2016
Cited alongside, same era.
Y. Malkov and D. Yashunin, “Efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. PP, no. 4, pp. 824–836, 03 2016
2016
Cited alongside, same era.
H. Wei, J. Yu, C. Lu, and X. Lin, “Speedup graph processing by graph ordering,” 06 2016, pp. 1813–1828
2016
Cited alongside, same era.
C. Fu and D. Cai, “Efanna : An extremely fast approximate nearest neighbor search algorithm based on knn graph,” 09 2016
2016
Cited alongside, same era.
2017
Cited alongside, same era.
C. Zhang, F. Wei, Q. Liu, Z. Tang, and Z. Li, “Graph edge partitioning via neighborhood heuristic,” 08 2017, pp. 605–614
2017
Cited alongside, same era.
J. Ren, M. Zhang, and D. Li, “Hm-ann: Efficient billion-point nearest neighbor search on heterogeneous memory,” Advances in Neural Information Processing Systems , vol. 33, pp. 10 672–10 684, 2020
2020
Later among the works it cites.
M. Wang, X. Xu, Q. Yue, and Y. Wang, “A comprehensive survey and experimental comparison of graph-based approximate nearest neighbor search,” Proceedings of the VLDB Endowment , vol. 14, pp. 1964–1978, 07 2021
2021
Later among the works it cites.
Q. Chen, B. Zhao, H. Wang, M. Li, C. Liu, Z. Li, M. Yang, and J. Wang, “Spann: Highly-efficient billion-scale approximate nearest neighborhood search,” Advances in Neural Information Processing Systems , vol. 34, pp. 5199–5212, 2021
2021
Later among the works it cites.
L. C. Shimomura, R. S. Oyamada, M. R. Vieira, and D. S. Kaster, “A survey on graph-based methods for similarity searches in metric spaces,” Information Systems , vol. 95, p. 101507, 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
X. Xu, M. Wang, Y. Wang, and D. Ma, “Two-stage routing with optimized guided search and greedy algorithm on proximity graph,” Knowledge-Based Systems , vol. 229, p. 107305, 07 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
Q. Wang, H. Yin, T. Chen, J. Yu, A. Zhou, and X. Zhang, “Fast-adapting and privacy-preserving federated recommender system,” The VLDB Journal , vol. 31, no. 5, pp. 877–896, 2022
2022
Later among the works it cites.
X. Xu, J. Liu, Y. Wang, and X. Ke, “Academic expert finding via ( k , p ) (k,p) -core based embedding over heterogeneous graphs,” in 2022 IEEE 38th International Conference on Data Engineering (ICDE) . IEEE, 2022, pp. 338–351
2022
Later among the works it cites.
H. Simhadri, G. Williams, M. Aumüller, M. Douze, A. Babenko, D. Baranchuk, Q. Chen, L. Hosseini, R. Krishnaswamy, G. Srinivasa, S. Subramanya, and J. Wang, “Results of the neurips’21 challenge on billion-scale approximate nearest neighbor search,” 05 2022
2022
Later among the works it cites.
J. Zhang, Z. Liu, W. Han, S. Xiao, R. Zheng, Y. Shao, H. Sun, H. Zhu, P. Srinivasan, W. Deng, Q. Zhang, and X. Xie, “Uni-retriever: Towards learning the unified embedding based retriever in bing sponsored search,” in KDD , 2022, pp. 4493–4501
2022
Later among the works it cites.
2022
Later among the works it cites.
2023
Closest in time.
S. Gollapudi, N. Karia, V. Sivashankar, R. Krishnaswamy, N. Begwani, S. Raz, Y. Lin, Y. Zhang, N. Mahapatro, P. Srinivasan, A. Singh, and H. V. Simhadri, “Filtered-diskann: Graph algorithms for approximate nearest neighbor search with filters,” in Proceedings of the ACM Web Conference 2023 , 2023, p. 3406–3416
2023
Closest in time.
Crawl. (2023) Common crawl. [Online]. Available: http://commoncrawl.org/
2023
Closest in time.
microsoft, “Sptag: A library for fast approximate nearest neighbor search,” https://github.com/microsoft/SPTAG, 2023
2023
Closest in time.
Zilliztech, “Bbann: Block-based approximate nearest neighbor,” https://github.com/zilliztech/BBAnn, 2023
2023
Closest in time.
microsoft, “Diskann,” https://github.com/microsoft/DiskANN, 2023
2023
Closest in time.
A. Yandex and V. Lempitsky, “Efficient indexing of billion-scale datasets of deep descriptors,” 06 2016, pp. 2055–2063
2063
Closest in time.