Graph Neural Networks (GNNs) are deep-learning architectures designed for
graph-type data, where understanding relationships among individual
observations is crucial. However, achieving promising GNN performance,
especially on unseen data, requires comprehensive hyperparameter tuning and
meticulous training. Unfortunately, these processes come with high
computational costs and significant human effort. Additionally, conventional
searching algorithms such as grid search may result in overfitting on
validation data, diminishing generalization accuracy. To tackle these
challenges, we propose a graph conditional latent diffusion framework
(GNN-Diff) to generate high-performing GNNs directly by learning from
checkpoints saved during a light-tuning coarse search. Our method: (1)
unleashes GNN training from heavy tuning and complex search space design; (2)
produces GNN parameters that outperform those obtained through comprehensive
grid search; and (3) establishes higher-quality generation for GNNs compared to
diffusion frameworks designed for general neural networks.

为了解决图神经网络的高计算成本和高人力成本问题，本研究提出了一种图条件潜在扩散框架（GNN-Diff），通过从粗略搜索中学习保存的检查点直接生成高性能的图神经网络，不需进行复杂的调参和搜索空间设计，比传统的扩散框架在生成高质量的图神经网络方面具有更好的表现。