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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)

Paper ID
7338
Venue
SIGMOD
Year
2026
Pagerank
4.1945683e-05
Overall Rank
10,033 | 30.21%
DOI
10.1145/3749181

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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
283 Querying K-Truss Community in Large and Dynamic Graphs 2014 SIGMOD 0.00029041257
7,268 Top-K Structural Diversity Search in Large Networks 2013 VLDB 4.7817823e-05
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