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
- 2. Miao Qiao
- 3. Rong-Hua Li
- 4. Hongchao Qin
- 5. Guoren Wang
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 284 | Querying K-Truss Community in Large and Dynamic Graphs | 2014 | SIGMOD | 0.00029014359 |
| 7,267 | Top-K Structural Diversity Search in Large Networks | 2013 | VLDB | 4.7771943e-05 |
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