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The ParClusterers Benchmark Suite (PCBS): A Fine-Grained Analysis of Scalable Graph Clustering

Summary: PCBS unifies scalable parallel algorithms and tooling for community detection, classification, and dense-subgraph mining, enabling fine-grained runtime/quality comparisons. Its methods are 4× faster than competing libraries; LambdaCC often achieves best quality. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
14429
Venue
VLDB
Year
2025
Pagerank
5.093636e-05
Overall Rank
11,102 | 23.84%
DOI
10.14778/3712221.3712246

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BibTeX Citation

@article{yu_vldb25,
        title = {{The ParClusterers Benchmark Suite (PCBS): A Fine-Grained Analysis of Scalable Graph Clustering}},
        author = {Yu, Shangdi and Shi, Jessica and Meindl, Jamison and Eisenstat, David and Ju, Xiaoen and Tavakkol, Sasan and Dhulipala, Laxman and Łącki, Jakub and Mirrokni, Vahab and Shun, Julian},
        journal = {PVLDB},
        series = {{VLDB} '25},
        volume = {18},
        number = {3},
        pages = {836--849},
        doi = {10.14778/3712221.3712246},
        url = {https://doi.org/10.14778/3712221.3712246},
        year = {2025}
}

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