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
- 2. Guanxian Jiang
- 3. Xiao Yan
- 4. Peiqi Yin
- 5. Qihui Zhou
- 6. James Cheng
Incoming Citations (Sorted by Pagerank)
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| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,472 | CARINA: An Efficient CXL-Oriented Embedding Serving System for Recommendation Models | 2025 | SIGMOD | 4.1945683e-05 |
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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 |
|---|---|---|---|---|
| 2,400 | ByteGNN: Efficient Graph Neural Network Training at Large Scale | 2022 | VLDB | 8.8955105e-05 |
| 2,677 | HET: Scaling out Huge Embedding Model Training via Cache-enabled Distributed Framework | 2022 | VLDB | 8.3268401e-05 |
| 5,377 | Parallel Training of Knowledge Graph Embedding Models: A Comparison of Techniques | 2022 | VLDB | 5.5410858e-05 |
| 5,538 | Growing and Serving Large Open-domain Knowledge Graphs | 2023 | SIGMOD | 5.4509524e-05 |
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