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)
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Authors
- 1. Ilie Sarpe
- 2. Aristides Gionis
Incoming Citations (Sorted by Pagerank)
Showing 0 of 0 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 5 of 5 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,257 | Influential Community Search in Large Networks | 2015 | VLDB | 0.00013020648 |
| 3,410 | Motivo: fast motif counting via succinct color coding and adaptive sampling | 2019 | VLDB | 7.1253867e-05 |
| 3,778 | A Learned Sketch for Subgraph Counting | 2021 | SIGMOD | 6.7747398e-05 |
| 4,898 | On Sampling from Massive Graph Streams | 2017 | VLDB | 5.8459467e-05 |
| 7,934 | Fast Local Subgraph Counting | 2024 | VLDB | 4.613363e-05 |
Previous
Page 1 / 1
Next