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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)

Paper ID
6910
Venue
SIGMOD
Year
2024
Pagerank
5.1558402e-05
Overall Rank
10,087 | 30.80%
DOI
10.1145/3639294

Incoming Non-self Citations Over Time

Authors

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)

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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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