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Boosting Graph Similarity Search through Pre-Computation

Summary: Introduces Nass, a pre-computation-driven graph-similarity framework that stores inter-graph GEDs to prune candidates in filtering. Coupled with an efficient GED computation module, Nass minimizes verifications and outperforms prior work. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6123
Venue
SIGMOD
Year
2021
Pagerank
5.6872619e-05
Overall Rank
7,152 | 50.94%
DOI
10.1145/3448016.3452780

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{kim_sigmod21,
        title = {{Boosting Graph Similarity Search through Pre-Computation}},
        author = {Kim, Jongik},
        series = {{SIGMOD} '21},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3448016.3452780},
        url = {https://dl.acm.org/doi/10.1145/3448016.3452780},
        year = {2021}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
3,906 FedKNN: Secure Federated k-Nearest Neighbor Search 2024 SIGMOD 7.0287643e-05
8,126 Computing Approximate Graph Edit Distance via Optimal Transport 2025 SIGMOD 5.4827332e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 8 of 8 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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