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A Particle-and-Density Based Evolutionary Clustering Method for Dynamic Networks

Summary: Particle-and-density evolutionary clustering for dynamic networks; nano-communities and quasi l-KK cliques support variable forming/dissolving communities. Information-theoretic mapping to quasi l-KK with cost embedding yields temporally smoothed, data-aligned clusters and stage detection (evolving/forming/dissolving) with 10x speedups. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
9891
Venue
VLDB
Year
2009
Pagerank
6.730222e-05
Overall Rank
3,817 | 73.45%
DOI
-

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