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Beyond Maximum Common Subgraph: A Framework Maximizing Shared Computation for Multi-Query Subgraph Matching
Summary: MASC avoids costly maximum-common-subgraph discovery, instead maximizing candidate-level reuse through signature-based joint filtering and a visit-only-once paradigm. Shared-block reuse exploits multiple overlaps among queries, delivering up to two orders of magnitude speedup in batch subgraph matching.
(summarized by gpt-5.6-luna on Jul 26 2026)
Paper ID
h50fef9c13f3aaf42
Venue
SIGMOD
Year
2026
Pagerank
4.9793485e-05
Overall Rank
10,420 | 29.95%
DOI
10.1145/3802022
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BibTeX Citation
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@inproceedings{zhang_sigmod26,
title = {{Beyond Maximum Common Subgraph: A Framework Maximizing Shared Computation for Multi-Query Subgraph Matching}},
author = {Zhang, Zhijie and Yang, Sitao and Zheng, Weiguo and Qiang, Zhou and Hong, Cuntao},
series = {{SIGMOD} '26},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3802022},
url = {https://dl.acm.org/doi/10.1145/3802022},
year = {2026}
}
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Showing 34 of 34 cited papers.
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