Output Space Sampling for Graph Patterns
Summary: Metropolis-Hastings-based framework to sample the output space of frequent subgraphs in graph pattern mining. Demonstrates versatility across sampling strategies, yielding representative, discriminative, and scalable subgraphs for efficient graph mining. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
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Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6,760 | GAIA: Graph Classification Using Evolutionary Computation | 2010 | SIGMOD | 4.9349071e-05 |
| 8,210 | Mining Top-k Pairs of Correlated Subgraphs in a Large Network | 2020 | VLDB | 4.5581054e-05 |
| 11,039 | Efficient Discovery of Significant Patterns with Few-Shot Resampling | 2024 | VLDB | 4.1945683e-05 |
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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 |
|---|---|---|---|---|
| 473 | Sampling Large Databases for Association Rules | 1996 | VLDB | 0.0002233798 |
| 1,747 | Mining Significant Graph Patterns by Leap Search | 2008 | SIGMOD | 0.00010691242 |
| 3,055 | Mining Compressed Frequent-Pattern Sets | 2005 | VLDB | 7.6448739e-05 |
| 3,454 | Traversing Itemset Lattices with Statistical Metric Pruning | 2000 | PODS | 7.0778482e-05 |
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