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T-FSM: A Task-Based System for Massively Parallel Frequent Subgraph Pattern Mining from a Big Graph

Summary: T-FSM's task-based parallel engine delivers high concurrency with bounded memory and balanced load for large-scale subgraph mining. It introduces Fraction-Score, a more accurate anti-monotonic measure than MNI, with speedups and open-source release. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6639
Venue
SIGMOD
Year
2023
Pagerank
5.4170414e-05
Overall Rank
8,478 | 41.84%
DOI
10.1145/3588928

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{yuan_sigmod23,
        title = {{T-FSM: A Task-Based System for Massively Parallel Frequent Subgraph Pattern Mining from a Big Graph}},
        author = {Yuan, Lyuheng and Yan, Da and Qu, Wenwen and Adhikari, Saugat and Khalil, Jalal and Long, Cheng and Wang, Xiaoling},
        series = {{SIGMOD} '23},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3588928},
        url = {https://dl.acm.org/doi/10.1145/3588928},
        year = {2023}
}

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