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Flexible and Feasible Support Measures for Mining Frequent Patterns in Large Labeled Graphs

Summary: Proposes a unified hypergraph framework for support measures in single-graph mining. Introduces MI and MVC measures; MI is linear-time computable; min-image-based measure bounds MI; MVC NP-hard but constant-factor approximable, with relaxations and bounds. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
ha6f11b349ab7c168
Venue
SIGMOD
Year
2017
Pagerank
5.4019783e-05
Overall Rank
8,037 | 45.97%
DOI
10.1145/3035918.3035936

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{meng_sigmod17,
        title = {{Flexible and Feasible Support Measures for Mining Frequent Patterns in Large Labeled Graphs}},
        author = {Meng, Jinghan and Tu, Yi-Cheng},
        series = {{SIGMOD} '17},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3035918.3035936},
        url = {https://dl.acm.org/doi/10.1145/3035918.3035936},
        year = {2017}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
8,891 Efficient Top-k Frequent Subgraph Mining Using Tight Upper and Lower Bounds 2025 VLDB 5.2559789e-05
10,786 MDS-FSM: Coverage-Based Frequent Subgraph Mining in Single Graphs 2026 VLDB 4.9793485e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 1 of 1 cited papers.

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

Rank Cited Paper Year Venue Pagerank
1,027 GraMI: Frequent Subgraph and Pattern Mining in a Single Large Graph 2014 VLDB 0.00012422544
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