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: 5364
Venue: SIGMOD
Year: 2017
Pagerank: 5.2351259e-05
Overall Rank: 9,703 | 33.43%
DOI: 10.1145/3035918.3035936

Incoming Non-self Citations Over Time

Authors

1. Jinghan Meng (University of South Florida)
2. Yi-Cheng Tu (University of South Florida)

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 1 of 1 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
11,072	Efficient Top-k Frequent Subgraph Mining Using Tight Upper and Lower Bounds	2025	VLDB	5.093636e-05

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
1,085	GraMI: Frequent Subgraph and Pattern Mining in a Single Large Graph	2014	VLDB	0.0001225302

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