GE2: A General and Efficient Knowledge Graph Embedding Learning System
Summary: GE2 is a general graph-embedding training system with a unified execution model/API for diverse negative sampling schemes. Key systems contribution: GPU-centric execution plus COVER, a CPU–multi-GPU swap algorithm that cuts communication/CPU overhead and yields 2–7.5x faster training than prior systems. (summarized by gpt-5.4-mini on May 24 2026)
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Authors
- 1. Chenguang Zheng (Chinese University of Hong Kong)
- 2. Guanxian Jiang (Chinese University of Hong Kong)
- 3. Xiao Yan (Centre for Perceptual and Interactive Intelligence)
- 4. Peiqi Yin (Chinese University of Hong Kong)
- 5. Qihui Zhou (Chinese University of Hong Kong)
- 6. James Cheng (Chinese University of Hong Kong)
BibTeX Citation
@inproceedings{zheng_sigmod24,
title = {{GE2: A General and Efficient Knowledge Graph Embedding Learning System}},
author = {Zheng, Chenguang and Jiang, Guanxian and Yan, Xiao and Yin, Peiqi and Zhou, Qihui and Cheng, James},
series = {{SIGMOD} '24},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3654986},
url = {https://dl.acm.org/doi/10.1145/3654986},
year = {2024}
}Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,251 | GPS: Revisiting the Data Layout for Disk-based High-Dimensional Vector Search | 2026 | SIGMOD | 5.093636e-05 |
| 10,420 | SG-Serve: Efficient Model Serving for Subgraph-based Graph Representation Learning | 2026 | SIGMOD | 5.093636e-05 |
| 10,752 | CARINA: An Efficient CXL-Oriented Embedding Serving System for Recommendation Models | 2025 | SIGMOD | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 4 of 4 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,863 | ByteGNN: Efficient Graph Neural Network Training at Large Scale | 2022 | VLDB | 9.5950349e-05 |
| 2,485 | HET: Scaling out Huge Embedding Model Training via Cache-enabled Distributed Framework | 2022 | VLDB | 8.5145736e-05 |
| 5,305 | Parallel Training of Knowledge Graph Embedding Models: A Comparison of Techniques | 2022 | VLDB | 6.2732352e-05 |
| 5,652 | Growing and Serving Large Open-domain Knowledge Graphs | 2023 | SIGMOD | 6.1335866e-05 |
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