Efficient and Adaptive Estimation of Local Triadic Coefficients
Summary: Introduces Triad, an adaptive sampling algorithm with a new class of unbiased estimators to efficiently estimate average local triadic coefficients (local clustering/closure) for node partitions without listing triangles. Provides provable sample-complexity bounds, scalable implementation for large graphs, and a case study showing detection of high-order collaboration patterns. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Ilie Sarpe
- 2. Aristides Gionis
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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 |
|---|---|---|---|---|
| 976 | Influential Community Search in Large Networks | 2015 | VLDB | 0.00012949951 |
| 2,982 | Motivo: fast motif counting via succinct color coding and adaptive sampling | 2019 | VLDB | 7.9470846e-05 |
| 3,229 | A Learned Sketch for Subgraph Counting | 2021 | SIGMOD | 7.6920894e-05 |
| 4,540 | On Sampling from Massive Graph Streams | 2017 | VLDB | 6.7090838e-05 |
| 8,117 | Fast Local Subgraph Counting | 2024 | VLDB | 5.5419908e-05 |
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