mlsanityData to 14 Oct 2026

GNNFlow: A Distributed Framework for Continuous Temporal GNN Learning on Dynamic Graphs

arXiv 2311.17410 ↗First posted 29 Nov 2023cs.DC cs.LGcited by 5

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Abstract (arXiv)

Graph Neural Networks (GNNs) play a crucial role in various fields. However, most existing deep graph learning frameworks assume pre-stored static graphs and do not support training on graph streams. In contrast, many real-world graphs are dynamic and contain time domain information. We introduce GNNFlow, a distributed framework that enables efficient continuous temporal graph representation learning on dynamic graphs on multi-GPU machines. GNNFlow introduces an adaptive time-indexed block-based data structure that effectively balances memory usage with graph update and sampling operation efficiency. It features a hybrid GPU-CPU graph data placement for rapid GPU-based temporal neighborhood sampling and kernel optimizations for enhanced sampling processes. A dynamic GPU cache for node and edge features is developed to maximize cache hit rates through reuse and restoration strategies. GNNFlow supports distributed training across multiple machines with static scheduling to ensure load balance. We implement GNNFlow based on DGL and PyTorch. Our experimental results show that GNNFlow provides up to 21.1x faster continuous learning than existing systems.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 21 of its 71 references that are in the map. First posted on arXiv 29 Nov 2023.

Builds on

21 of 71 references.

References · 21

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9 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 3; 2025: 1; 2026: 1.

5 papers.

  1. 2024A Comprehensive Survey of Dynamic Graph Neural Networks: Models, Frameworks, Benchmarks, Experiments and Challengescited by 30
  2. 2024MSPipe: Efficient Temporal GNN Training via Staleness-Aware Pipelinecited by 12
  3. 2024Towards Ideal Temporal Graph Neural Networks: Evaluations and Conclusions after 10,000 GPU Hourscited by 2
  4. 2025GNN-Suite: a Graph Neural Network Benchmarking Framework for Biomedical Informaticscited by 2
  5. 2026Directly Optimizing Mean Demographic Parity for Nonlinear Regressioncited by 0