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Comprehensive Evaluation of GNN Training Systems: A Data Management Perspective

Summary: Comprehensive empirical study of GNN training systems from a data-management perspective, quantifying how graph partitioning, mini-batch preparation, and CPU–GPU data movement dominate training cost. Extensive benchmarks expose trade-offs across approaches and provide practical system-design guidelines. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
13559
Venue
VLDB
Year
2024
Pagerank
6.2687017e-05
Overall Rank
5,316 | 63.53%
DOI
10.14778/3648160.3648167

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{yuan_vldb24,
        title = {{Comprehensive Evaluation of GNN Training Systems: A Data Management Perspective}},
        author = {Yuan, Hao and Liu, Yajiong and Zhang, Yanfeng and Ai, Xin and Wang, Qiange and Chen, Chaoyi and Gu, Yu and Yu, Ge},
        journal = {PVLDB},
        series = {{VLDB} '24},
        volume = {17},
        number = {6},
        pages = {1241--1254},
        doi = {10.14778/3648160.3648167},
        url = {https://doi.org/10.14778/3648160.3648167},
        year = {2024}
}

Incoming Citations (Sorted by Pagerank)

Showing 8 of 8 citing papers.

Rank Citing Paper Year Venue Pagerank
9,546 NeutronTP: Load-Balanced Distributed Full-Graph GNN Training with Tensor Parallelism 2025 VLDB 5.2528121e-05
10,309 A Comprehensive Benchmark on Spectral GNNs: The Impact on Efficiency, Memory, and Effectiveness 2026 SIGMOD 5.093636e-05
10,357 DepCache: A KV Cache Management Framework for GraphRAG with Dependency Attention 2026 SIGMOD 5.093636e-05
10,521 Efficient GNN Training on Giant Graphs with Collective Batching and Scheduling 2026 VLDB 5.093636e-05
10,811 Graph Neural Network Training Systems: A Performance Comparison of Full-Graph and Mini-Batch 2025 VLDB 5.093636e-05
10,890 Heta: Distributed Training of Heterogeneous Graph Neural Networks 2025 VLDB 5.093636e-05
10,899 Can Graph Reordering Speed Up Graph Neural Network Training? An Experimental Study 2025 VLDB 5.093636e-05
10,975 Faster Convergence in Mini-batch Graph Neural Networks Training with Pseudo Full Neighborhood Compensation 2025 VLDB 5.093636e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 9 of 9 cited papers.

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

Rank Cited Paper Year Venue Pagerank
223 AliGraph: A Comprehensive Graph Neural Network Platform 2019 VLDB 0.00024182473
1,048 AGL: A Scalable System for Industrial-purpose Graph Machine Learning 2020 VLDB 0.00012433693
1,132 SANCUS: Staleness-Aware Communication-Avoiding Full-Graph Decentralized Training in Large-Scale Graph Neural Networks 2022 VLDB 0.00012041292
1,234 Large Graph Convolutional Network Training with GPU-Oriented Data Communication Architecture 2021 VLDB 0.00011549432
1,863 ByteGNN: Efficient Graph Neural Network Training at Large Scale 2022 VLDB 9.5950349e-05
2,640 Scalable and Efficient Full-Graph GNN Training for Large Graphs 2023 SIGMOD 8.3074486e-05
2,668 DUCATI: A Dual-Cache Training System for Graph Neural Networks on Giant Graphs with the GPU 2023 SIGMOD 8.2750247e-05
2,695 NeutronStar: Distributed GNN Training with Hybrid Dependency Management 2022 SIGMOD 8.2468134e-05
4,878 NeutronStream: A Dynamic GNN Training Framework with Sliding Window for Graph Streams 2024 VLDB 6.4684388e-05
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# Overall Rank Paper Year Venue
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