AGIS: Fast Approximate Graph Pattern Mining with Structure-Informed Sampling
Summary: AGIS uses structure-informed neighbor sampling, deriving and approximating an ideal pattern-aware sampling distribution instead of uniform sampling. Adaptive tradeoff between convergence and overhead yields huge speedups (28.5× mean, up to 100k×) and scales to graphs with tens of billions of edges for arbitrary patterns. (summarized by gpt-5-mini on Mar 13 2026)
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Authors
- 1. Seoyong Lee
- 2. Jinho Lee
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Outgoing Citations (Sorted by Pagerank)
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 327 | The Ubiquity of Large Graphs and Surprising Challenges of Graph Processing | 2018 | VLDB | 0.00027312381 |
| 1,348 | Counting and Sampling Triangles from a Graph Stream | 2013 | VLDB | 0.00012461666 |
| 1,724 | A General Framework for Estimating Graphlet Statistics via Random Walk | 2017 | VLDB | 0.00010736699 |
| 3,135 | Fractal: A General-Purpose Graph Pattern Mining System | 2019 | SIGMOD | 7.4928743e-05 |
| 3,488 | GPU-Accelerated Subgraph Enumeration on Partitioned Graphs | 2020 | SIGMOD | 7.0460627e-05 |
| 4,903 | On Sampling from Massive Graph Streams | 2017 | VLDB | 5.8403259e-05 |
| 6,288 | Cardinality Estimation of Subgraph Matching: A Filtering-Sampling Approach | 2024 | VLDB | 5.1226099e-05 |
| 7,317 | Accurate and Fast Approximate Graph Pattern Mining at Scale | 2025 | VLDB | 4.7593708e-05 |
| 9,040 | RUSH: Real-time Burst Subgraph Detection in Dynamic Graphs | 2024 | VLDB | 4.3997447e-05 |
| 9,056 | Behavior Query Discovery in System-Generated Temporal Graphs | 2016 | VLDB | 4.3997447e-05 |
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