Worst-Case-Optimal Similarity Joins on Graph Databases
Summary: Worst-case-optimal graph equijoins extended to k-NN similarity by fusing the data graph with a k-NN graph and using LTJ on a Ring to integrate similarity predicates. Wikidata–IMGpedia experiments show clear gains over a two-phase baseline, especially for dense similarity connections (up to an order of magnitude). (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Diego Arroyuelo (Millennium Institute for Foundational Data; Pontifical Catholic University of Chile)
- 2. Benjamin Bustos (Millennium Institute for Foundational Data; University of Chile)
- 3. Adrián Gómez-Brandón (Millennium Institute for Foundational Data; Research Center for Information and Communication Technologies; University of A Coruña)
- 4. Aidan Hogan (Millennium Institute for Foundational Data; University of Chile)
- 5. Gonzalo Navarro (Millennium Institute for Foundational Data; University of Chile)
- 6. Juan Reutter (Millennium Institute for Foundational Data; Pontifical Catholic University of Chile)
BibTeX Citation
@inproceedings{arroyuelo_sigmod24,
title = {{Worst-Case-Optimal Similarity Joins on Graph Databases}},
author = {Arroyuelo, Diego and Bustos, Benjamin and Gómez-Brandón, Adrián and Hogan, Aidan and Navarro, Gonzalo and Reutter, Juan},
series = {{SIGMOD} '24},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3639294},
url = {https://dl.acm.org/doi/10.1145/3639294},
year = {2024}
}
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 8,896 | Aster: Enhancing LSM-structures for Scalable Graph Database | 2025 | SIGMOD | 5.3495662e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 12 of 12 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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