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Accelerating Maximum Common Subgraph Computation by Exploiting Symmetries

Summary: Introduces dual-symmetry breaking for exact MCS, exploiting modular symmetries in both variable and value graphs via local neighborhoods. Prunes isomorphic search subtrees while preserving optimality, substantially outperforming RRSplit on benchmarks. (summarized by gpt-5.6-luna on Jul 26 2026)

Paper ID
7378
Venue
SIGMOD
Year
2026
Pagerank
5.093636e-05
Overall Rank
10,187 | 30.11%
DOI
10.1145/3802005

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BibTeX Citation

@inproceedings{kothalawala_sigmod26,
        title = {{Accelerating Maximum Common Subgraph Computation by Exploiting Symmetries}},
        author = {Kothalawala, Buddhi and Koehler, Henning and Farhan, Muhammad},
        series = {{SIGMOD} '26},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3802005},
        url = {https://dl.acm.org/doi/10.1145/3802005},
        year = {2026}
}

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

Rank Cited Paper Year Venue Pagerank
5,758 Fast Maximum Common Subgraph Search: A Redundancy-Reduced Backtracking Approach 2025 SIGMOD 6.0972035e-05
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