mlsanityData to 14 Oct 2026

Comprehensive Evaluation of GNN Training Systems: A Data Management Perspective

arXiv 2311.13279 ↗First posted 22 Nov 2023cs.LG cs.DCcited by 9

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Abstract (arXiv)

Many Graph Neural Network (GNN) training systems have emerged recently to support efficient GNN training. Since GNNs embody complex data dependencies between training samples, the training of GNNs should address distinct challenges different from DNN training in data management, such as data partitioning, batch preparation for mini-batch training, and data transferring between CPUs and GPUs. These factors, which take up a large proportion of training time, make data management in GNN training more significant. This paper reviews GNN training from a data management perspective and provides a comprehensive analysis and evaluation of the representative approaches. We conduct extensive experiments on various benchmark datasets and show many interesting and valuable results. We also provide some practical tips learned from these experiments, which are helpful for designing GNN training systems in the future.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 25 of its 72 references that are in the map. First posted on arXiv 22 Nov 2023.

Builds on

25 of 72 references.

References · 25

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13 more
  1. 2021Dorylus: Affordable, Scalable, and Accurate GNN Training with Distributed CPU Servers and Serverless Threadscited by 51
  2. 2021BGL: GPU-Efficient GNN Training by Optimizing Graph Data I/O and Preprocessingcited by 50
  3. 2022BNS-GCN: Efficient Full-Graph Training of Graph Convolutional Networks with Partition-Parallelism and Random Boundary Node Samplingcited by 43
  4. 2022Distributed Graph Neural Network Training: A Surveycited by 32
  5. 2022MGG: Accelerating Graph Neural Networks with Fine-grained intra-kernel Communication-Computation Pipelining on Multi-GPU Platformscited by 16
  6. 2023Communication-Efficient Graph Neural Networks with Probabilistic Neighborhood Expansion Analysis and Cachingcited by 16
  7. 2022A Comprehensive Survey on Distributed Training of Graph Neural Networkscited by 15
  8. 2023The Evolution of Distributed Systems for Graph Neural Networks and their Origin in Graph Processing and Deep Learning: A Surveycited by 14
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  10. 2022SUGAR: Efficient Subgraph-level Training via Resource-aware Graph Partitioningcited by 6
  11. 2017Stochastic Training of Graph Convolutional Networks with Variance Reductioncited by 5
  12. 2023An Experimental Comparison of Partitioning Strategies for Distributed Graph Neural Network Trainingcited by 4
  13. 2023Legion: Automatically Pushing the Envelope of Multi-GPU System for Billion-Scale GNN Trainingcited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 4; 2025: 3; 2026: 2.

9 papers.

  1. 2024A Comprehensive Survey of Dynamic Graph Neural Networks: Models, Frameworks, Benchmarks, Experiments and Challengescited by 30
  2. 2024Graph Neural Network Training Systems: A Performance Comparison of Full-Graph and Mini-Batchcited by 10
  3. 2024A Comprehensive Benchmark on Spectral GNNs: The Impact on Efficiency, Memory, and Effectivenesscited by 6
  4. 2024Can Graph Reordering Speed Up Graph Neural Network Training? An Experimental Studycited by 4
  5. 2025Plexus: Taming Billion-edge Graphs with 3D Parallel Full-graph GNN Trainingcited by 1
  6. 2025HydroGAT: Distributed Heterogeneous Graph Attention Transformer for Spatiotemporal Flood Predictioncited by 1
  7. 2025A Roadmap for Applying Graph Neural Networks to Numerical Data: Insights from Cementitious Materialscited by 0
  8. 2026Not All Neighbors Matter: Understanding the Impact of Graph Sparsification on GNN Pipelinescited by 0
  9. 2026EmbedPart: Embedding-Driven Graph Partitioning for Scalable Graph Neural Network Trainingcited by 0