Approximate Pattern Matching in Massive Graphs with Precision and Recall Guarantees
Summary: Novel optimization opportunities enable a scalable pipeline for approximate graph pattern matching with 100% precision and recall for k-edit-distance subgraphs of a template. It fuses edit-distance matching with pruning, supports arbitrary patterns, and scales to graphs with hundreds of billions to trillions of edges. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Tahsin Reza
- 2. Matei Ripeanu
- 3. Geoffrey Sanders
- 4. Roger Pearce
Incoming Citations (Sorted by Pagerank)
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Outgoing Citations (Sorted by Pagerank)
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4 | Pregel: A System for Large-Scale Graph Processing | 2010 | SIGMOD | 0.0019005923 |
| 651 | Efficient Subgraph Matching on Billion Node Graphs | 2012 | VLDB | 0.00018648572 |
| 1,089 | GRAMI: Frequent Subgraph and Pattern Mining in a Single Large Graph | 2014 | VLDB | 0.00014157922 |
| 1,344 | Counting and Sampling Triangles from a Graph Stream | 2013 | VLDB | 0.00012473724 |
| 1,414 | Graph Pattern Matching: From Intractable to Polynomial Time | 2010 | VLDB | 0.00012118275 |
| 1,454 | SAPPER: Subgraph Indexing and Approximate Matching in Large Graphs | 2010 | VLDB | 0.00011913289 |
| 2,449 | GraphMat: High performance graph analytics made productive | 2015 | VLDB | 8.7915899e-05 |
| 4,330 | Mining Top-K Large Structural Patterns in a Massive Network | 2011 | VLDB | 6.2839861e-05 |
| 4,807 | Diversified Top-k Graph Pattern Matching | 2013 | VLDB | 5.9092289e-05 |
| 7,019 | Bridging the Gap Between HPC and Big Data Frameworks | 2017 | VLDB | 4.860057e-05 |
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