CompressGraph: Efficient Parallel Graph Analytics with Rule-Based Compression
Summary: CompressGraph is a rule-based graph analytics engine that exploits redundancy in neighbor sequences to accelerate analytics and save space. It enables reuse of intermediate results during traversal, offering expressiveness and parallelism on CPUs/GPUs. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Zheng Chen (Renmin University of China)
- 2. Feng Zhang (Renmin University of China)
- 3. Jiawei Guan (Renmin University of China)
- 4. Jidong Zhai (Tsinghua University)
- 5. Xipeng Shen (North Carolina State University)
- 6. Huanchen Zhang (Shanghai Qi Zhi Institute; Tsinghua University)
- 7. Wentong Shu (Renmin University of China)
- 8. Xiaoyong Du (Renmin University of China)
BibTeX Citation
@inproceedings{chen_sigmod23,
title = {{CompressGraph: Efficient Parallel Graph Analytics with Rule-Based Compression}},
author = {Chen, Zheng and Zhang, Feng and Guan, Jiawei and Zhai, Jidong and Shen, Xipeng and Zhang, Huanchen and Shu, Wentong and Du, Xiaoyong},
series = {{SIGMOD} '23},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3588684},
url = {https://dl.acm.org/doi/10.1145/3588684},
year = {2023}
}
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