  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.
