Gem: Scalable Monotonic Graph Processing Beyond Billion-Scale on a Single Machine
Summary: Gem: an out-of-core engine for monotonic graph algorithms using a compact PageRank sketch plus a novel abstraction to derive tight vertex- and partition-level bounds for aggressive pruning. Scales single-machine to 42.5B edges; up to 135× faster than GridGraph and 12× vs Wonderland. (summarized by gpt-5-mini on Feb 11 2026)
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Authors
- 1. Chengying Huan (Nanjing University)
- 2. Zhengyi Yang (University of New South Wales)
- 3. Haoshen Yang (Rutgers University)
- 4. Shaonan Ma (Qiyuan Lab)
- 5. Rong Gu (Nanjing University)
- 6. Fang Xi (Qiyuan Lab)
- 7. Yongchao Liu (Ant Financial)
- 8. Guihai Chen (Nanjing University)
- 9. Chen Tian (Nanjing University)
BibTeX Citation
@inproceedings{huan_sigmod26,
title = {{Gem: Scalable Monotonic Graph Processing Beyond Billion-Scale on a Single Machine}},
author = {Huan, Chengying and Yang, Zhengyi and Yang, Haoshen and Ma, Shaonan and Gu, Rong and Xi, Fang and Liu, Yongchao and Chen, Guihai and Tian, Chen},
series = {{SIGMOD} '26},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3769795},
url = {https://dl.acm.org/doi/10.1145/3769795},
year = {2026}
}
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