GREAT: Generalized Reservoir Sampling based Triangle Counting Estimation over Streaming Graphs
Summary: GRS: generalized reservoir sampling that stores fewer edges yet yields uniform random edge samples in streaming graphs, cutting memory and compute vs fixed-size samplers. GREAT estimates triangle counts using GRS; GREAT+ reweights sampling for timestamp-interval distributions, giving ≈10× lower relative error. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Siyue Wu
- 2. Dingming Wu
- 3. Sinhong Cheuk
- 4. Tsz Nam Chan
- 5. Kezhong Lu
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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 |
|---|---|---|---|---|
| 184 | New Sampling-Based Summary Statistics for Improving Approximate Query Answers | 1998 | SIGMOD | 0.00036625711 |
| 392 | Counting Triangles in Data Streams | 2006 | PODS | 0.00024556183 |
| 1,344 | Counting and Sampling Triangles from a Graph Stream | 2013 | VLDB | 0.00012473724 |
| 2,878 | Sampling Time-Based Sliding Windows in Bounded Space | 2008 | SIGMOD | 7.9706235e-05 |
| 3,063 | Sliding Window-based Approximate Triangle Counting over Streaming Graphs with Duplicate Edges | 2021 | SIGMOD | 7.6321424e-05 |
| 4,879 | Approximately Counting Triangles in Large Graph Streams Including Edge Duplicates with a Fixed Memory Usage | 2018 | VLDB | 5.8575676e-05 |
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