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This is a Quantitative Study study (also classified as: Experimental Study).

PDGNN: Efficient Micro-batch GNN Training via Degree-Pruned Partitioning and Redundancy Elimination

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Efficiently reduces training time and memory consumption in graph neural networks using micro-batch training.
Achieves up to 3.5 × speedup and 46% lower GPU memory use, demonstrating significant efficiency improvements.
Introduces degree-pruning-based partitioning to lower overhead by simplifying bipartite graphs.
Enhances redundancy elimination through dependency-aware techniques, allowing for cached computations.

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Proposed micro-batch training method improves efficiency, reducing memory use in GNN training on single machines.

Zhang et al. (2025) studied this question.

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