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 (Royal Institute of Technology)
- 2. Aristides Gionis (Royal Institute of Technology)
BibTeX Citation
@article{sarpe_vldb25,
title = {{Efficient and Adaptive Estimation of Local Triadic Coefficients}},
author = {Sarpe, Ilie and Gionis, Aristides},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {8},
pages = {2561--2574},
doi = {10.14778/3742728.3742748},
url = {https://doi.org/10.14778/3742728.3742748},
year = {2025}
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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 |
|---|---|---|---|---|
| 982 | Influential Community Search in Large Networks | 2015 | VLDB | 0.0001283311 |
| 3,028 | Motivo: fast motif counting via succinct color coding and adaptive sampling | 2019 | VLDB | 7.8349117e-05 |
| 3,283 | A Learned Sketch for Subgraph Counting | 2021 | SIGMOD | 7.56675e-05 |
| 4,613 | On Sampling from Massive Graph Streams | 2017 | VLDB | 6.6071509e-05 |
| 8,251 | Fast Local Subgraph Counting | 2024 | VLDB | 5.4574671e-05 |
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