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Parallel k-Core Decomposition: Theory and Practice

Summary: A simple, work-efficient parallel framework for k-core decomposition enabling high parallelism. It uses sampling to reduce contention on high-degree vertices, vertical granularity control to cut scheduling overhead for low-degree vertices, and a hierarchical bucket structure for high coreness, yielding strong gains over ParK/PKC/Julienne on 96-core graphs (up to 315x vs ParK). (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
7313
Venue
SIGMOD
Year
2025
Pagerank
5.093636e-05
Overall Rank
10,772 | 26.10%
DOI
10.1145/3725332

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BibTeX Citation

@inproceedings{liu_sigmod25,
        title = {{Parallel k-Core Decomposition: Theory and Practice}},
        author = {Liu, Youzhe and Dong, Xiaojun and Gu, Yan and Sun, Yihan},
        series = {{SIGMOD} '25},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3725332},
        url = {https://dl.acm.org/doi/10.1145/3725332},
        year = {2025}
}

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