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ByteGNN: Efficient Graph Neural Network Training at Large Scale

Summary: ByteGNN enables scalable distributed GNN training with three designs: mini-batch sampling for high parallelism; a two-level scheduler for better resource use; and a GNN-aware partitioner. 3.5–23.8x end-to-end speedups, 2–6x CPU, ~50% network-cost reduction. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
h3e125211f3c09628
Venue
VLDB
Year
2022
Pagerank
9.6792287e-05
Overall Rank
1,772 | 88.09%
DOI
10.14778/3514061.3514069

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{zheng_vldb22,
        title = {{ByteGNN: Efficient Graph Neural Network Training at Large Scale}},
        author = {Zheng, Chenguang and Chen, Hongzhi and Cheng, Yuxuan and Song, Zhezheng and Wu, Yifan and Li, Changji and Cheng, James and Yang, Hao and Zhang, Shuai},
        journal = {PVLDB},
        series = {{VLDB} '22},
        volume = {15},
        number = {6},
        pages = {1228--1242},
        doi = {10.14778/3514061.3514069},
        url = {https://doi.org/10.14778/3514061.3514069},
        year = {2022}
}

Incoming Citations (Sorted by Pagerank)

Showing 22 of 22 citing papers.

Rank Citing Paper Year Venue Pagerank
4,502 NeutronStream: A Dynamic GNN Training Framework with Sliding Window for Graph Streams 2024 VLDB 6.5740722e-05
4,764 ETC: Efficient Training of Temporal Graph Neural Networks over Large-scale Dynamic Graphs 2024 VLDB 6.4276208e-05
4,825 DAHA: Accelerating GNN Training with Data and Hardware Aware Execution Planning 2024 VLDB 6.3923554e-05
4,898 Comprehensive Evaluation of GNN Training Systems: A Data Management Perspective 2024 VLDB 6.365824e-05
6,826 SIMPLE: Efficient Temporal Graph Neural Network Training at Scale with Dynamic Data Placement 2024 SIGMOD 5.6700316e-05
7,522 NeutronTask: Scalable and Efficient Multi-GPU GNN Training with Task Parallelism 2025 VLDB 5.5025409e-05
7,708 Systems for Scalable Graph Analytics and Machine Learning: Trends and Methods 2025 VLDB 5.4718627e-05
8,064 Distributed Graph Embedding with Information-Oriented Random Walks 2023 VLDB 5.3954618e-05
8,152 NeutronTP: Load-Balanced Distributed Full-Graph GNN Training with Tensor Parallelism 2025 VLDB 5.3896512e-05
8,670 Fight Fire with Fire: Towards Robust Graph Neural Networks on Dynamic Graphs via Actively Defense 2024 VLDB 5.2917782e-05
9,233 Can Graph Reordering Speed Up Graph Neural Network Training? An Experimental Study 2025 VLDB 5.2056825e-05
9,234 Effective and Efficient Distributed Temporal Graph Learning through Hotspot Memory Sharing 2025 VLDB 5.2056825e-05
9,795 NeutronHeter: Optimizing Distributed Graph Neural Network Training for Heterogeneous Clusters 2026 SIGMOD 5.1257999e-05
9,900 Scalable Graph Convolutional Network Training on Distributed-Memory Systems 2023 VLDB 5.1116429e-05
10,043 The Image Calculator: 10x Faster Image-AI Inference by Replacing JPEG with Self-designing Storage Format 2024 SIGMOD 5.0921006e-05
10,257 Graph Neural Network Training Systems: A Performance Comparison of Full-Graph and Mini-Batch 2025 VLDB 5.050482e-05
10,520 A Comprehensive Benchmark on Spectral GNNs: The Impact on Efficiency, Memory, and Effectiveness 2026 SIGMOD 4.9793485e-05
10,609 SG-Serve: Efficient Model Serving for Subgraph-based Graph Representation Learning 2026 SIGMOD 4.9793485e-05
10,826 FeLoG: Scalable and Efficient Distributed Graph Embedding with Feedback Loop Mechanism 2026 VLDB 4.9793485e-05
11,043 NeutronCloud: Resource-Aware Distributed GNN Training in Fluctuating Cloud Environments 2026 VLDB 4.9793485e-05
11,289 Heta: Distributed Training of Heterogeneous Graph Neural Networks 2025 VLDB 4.9793485e-05
11,530 GE2: A General and Efficient Knowledge Graph Embedding Learning System 2024 SIGMOD 4.9793485e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 6 of 6 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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