New research analyzes full-graph vs. mini-batch GNN training

This paper presents a comprehensive analysis comparing full-graph and mini-batch training for Graph Neural Networks (GNNs). It explores the impact of batch size and fan-out size on GNN convergence and generalization, offering theoretical and empirical insights. The research introduces a novel generalization analysis using Wasserstein distance and highlights non-isotropic effects of these parameters, suggesting that full-graph training is not always superior to well-tuned mini-batch settings. AI

影响 Provides theoretical and empirical guidance for tuning GNN training hyperparameters, potentially improving efficiency and performance.

排序理由 Academic paper analyzing GNN training methodologies. [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

• Graph Neural Network
• Mengfan Liu
• Wasserstein distance

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