Efficient k-Clique Count Estimation with Accuracy Guarantee
Summary: SR-kCCE pre-determines s, the number of uniformly sampled k-cliques required to meet a provable accuracy guarantee and to estimate sampling time. It stops refining the superset sample space when elapsed time ≈ estimated sampling time, balancing construction vs sampling and enabling efficient u.a.r. sampling. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Lijun Chang (University of Sydney)
- 2. Rashmika Gamage (University of Sydney)
- 3. Jeffrey Xu Yu (Chinese University of Hong Kong)
BibTeX Citation
@article{chang_vldb24,
title = {{Efficient k-Clique Count Estimation with Accuracy Guarantee}},
author = {Chang, Lijun and Gamage, Rashmika and Yu, Jeffrey Xu},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {11},
pages = {3707--3719},
doi = {10.14778/3681954.3682032},
url = {https://doi.org/10.14778/3681954.3682032},
year = {2024}
}Incoming Citations (Sorted by Pagerank)
Showing 5 of 5 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6,933 | Maximum Defective Clique Computation: Improved Time Complexities and Practical Performance | 2025 | VLDB | 5.6252271e-05 |
| 8,913 | Efficient Defective Clique Enumeration and Search with Worst-Case Optimal Search Space | 2026 | SIGMOD | 5.2361195e-05 |
| 10,395 | Efficient Querying of Maximum Connectivity-Based Quasi-Cliques in Large Graphs | 2027 | SIGMOD | 4.9605343e-05 |
| 10,641 | Estimating Biclique Counts with Accuracy Guarantees | 2026 | SIGMOD | 4.9605343e-05 |
| 10,860 | CREST: Approximate k-Clique Counting in Real-World Networks via Refinement of Star-Based Sample Space | 2026 | VLDB | 4.9605343e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 3 of 3 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,198 | KClist++: A Simple Algorithm for Finding k-Clique Densest Subgraphs in Large Graphs | 2020 | VLDB | 0.00011541919 |
| 3,396 | Scaling Up k-Clique Densest Subgraph Detection | 2023 | SIGMOD | 7.3277458e-05 |
| 3,597 | Efficient k-Clique Listing: An Edge-Oriented Branching Strategy | 2024 | SIGMOD | 7.1598365e-05 |
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