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
- 2. Yi-Cheng Tu
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,848 | Efficient Top-k Frequent Subgraph Mining Using Tight Upper and Lower Bounds | 2025 | VLDB | 4.1945683e-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 |
|---|---|---|---|---|
| 1,089 | GRAMI: Frequent Subgraph and Pattern Mining in a Single Large Graph | 2014 | VLDB | 0.00014157922 |
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