Scaling Up k-Clique Percolation Community Detection
Summary: Introduces Quasi-KCPC—an incomplete KCPC obtainable during maximal-clique enumeration—and two scalable KCPC algorithms: a Quasi-KCPC–pruned maximal-clique-adjacency traversal and a (k−1)-clique listing approach that assembles k-cliques via maximal-clique links. Adds incremental vertex/edge update routines and reports up to ~100× speedups over prior KCPC methods on 12 large real graphs. (summarized by gpt-5-mini on Feb 11 2026)
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Authors
- 1. Yue Zeng (Beijing Institute of Technology)
- 2. Miao Qiao (University of Auckland)
- 3. Rong-Hua Li (Beijing Institute of Technology)
- 4. Hongchao Qin (Beijing Institute of Technology)
- 5. Guoren Wang (Beijing Institute of Technology)
BibTeX Citation
@inproceedings{zeng_sigmod26,
title = {{Scaling Up k-Clique Percolation Community Detection}},
author = {Zeng, Yue and Qiao, Miao and Li, Rong-Hua and Qin, Hongchao and Wang, Guoren},
series = {{SIGMOD} '26},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3749181},
url = {https://dl.acm.org/doi/10.1145/3749181},
year = {2026}
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 189 | Querying K-Truss Community in Large and Dynamic Graphs | 2014 | SIGMOD | 0.00026114928 |
| 7,094 | Top-K Structural Diversity Search in Large Networks | 2013 | VLDB | 5.7055799e-05 |
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