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)
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Authors
- 1. Youzhe Liu (University of California Riverside)
- 2. Xiaojun Dong (University of California Riverside)
- 3. Yan Gu (University of California Riverside)
- 4. Yihan Sun (University of California Riverside)
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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Showing 6 of 6 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 793 | Streaming Algorithms for k-core Decomposition | 2013 | VLDB | 0.00013978774 |
| 825 | K-Core Decomposition of Large Networks on a Single PC | 2016 | VLDB | 0.00013779895 |
| 3,855 | Distributed D-core Decomposition over Large Directed Graphs | 2022 | VLDB | 7.0712144e-05 |
| 6,368 | Theoretically and Practically Efficient Parallel Nucleus Decomposition | 2022 | VLDB | 5.8975125e-05 |
| 6,617 | Efficient Parallel D-core Decomposition at Scale | 2024 | VLDB | 5.8212982e-05 |
| 11,394 | Parallel Strong Connectivity Based on Faster Reachability | 2023 | SIGMOD | 5.093636e-05 |
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