Scaling Up Density Decomposition on Massive Graphs
Summary: Introduces MeanDC and CoreDC to cut max-flow work via mean pivots and k-core prepartitioning, then parallel HeatDC/CoreHeatDC to extract layers with edge-local heat diffusion. CoreHeatDC decomposes graphs with 1.81B edges in hundreds of seconds. (summarized by gpt-6-luna on Oct 22 2026)
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Authors
- 1. Yalong Zhang (Beijing Institute of Technology)
- 2. Rong-Hua Li (Beijing Institute of Technology)
- 3. Qi Zhang (University of Science and Technology Beijing)
- 4. Jiaqi Jiang (Beijing Institute of Technology)
- 5. Qiangqiang Dai (Beijing Institute of Technology)
- 6. Guoren Wang (Beijing Institute of Technology)
BibTeX Citation
@inproceedings{zhang_sigmod27,
title = {{Scaling Up Density Decomposition on Massive Graphs}},
author = {Zhang, Yalong and Li, Rong-Hua and Zhang, Qi and Jiang, Jiaqi and Dai, Qiangqiang and Wang, Guoren},
series = {{SIGMOD} '27},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3837122},
url = {https://dl.acm.org/doi/10.1145/3837122},
year = {2027}
}
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