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)
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 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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