Mining Significant Graph Patterns by Leap Search
Summary: Introduces LEAP, a framework for mining the most significant graph patterns under general, non-antimonotonic objectives. Structural leap search and frequency-descending mining rapidly identify highly significant patterns, outperforming branch-and-bound and enabling effective graph classification. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Xifeng Yan
- 2. Hong Cheng
- 3. Jiawei Han
- 4. Philip S. Yu
Incoming Citations (Sorted by Pagerank)
Showing 16 of 16 citing papers.
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Outgoing Citations (Sorted by Pagerank)
Showing 4 of 4 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 13 | Mining Association Rules between Sets of Items in Large Databases | 1993 | SIGMOD | 0.0010864752 |
| 203 | Graph Indexing: A Frequent Structure-based Approach | 2004 | SIGMOD | 0.00034889335 |
| 350 | FG-Index: Towards Verification-Free Query Processing on Graph Databases | 2007 | SIGMOD | 0.00026365067 |
| 3,454 | Traversing Itemset Lattices with Statistical Metric Pruning | 2000 | PODS | 7.0778482e-05 |
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