用扩散模型生成高性能GNN，少调参也能提效。
Diffusing to the Top: Boost Graph Neural Networks with Minimal Hyperparameter Tuning
- 基于低质量参数点，用扩散框架生成优化GNN。
- 166组实验验证，性能提升稳定且泛化性强。
- 适合想省调参时间的研究者和工程落地场景。
图神经网络（GNN）在图表示学习中表现优异，但在复杂任务如大规模图和长程图的节点分类上，需大量超参数调优才能发挥最佳性能，带来高计算与时间成本。本文提出一种图条件化的潜在扩散框架（GNN-Diff），通过轻量级粗调搜索选定的次优超参数模型检查点，生成高性能的GNN。我们在四个图任务上进行了166次实验：小图、大图、长程图的节点分类以及链接预测，涵盖10种经典与前沿目标模型及20个公开数据集。结果一致表明：(1) GNN-Diff能以高效方式提升GNN性能；(2) 在多次生成中对未见数据表现出高稳定性和泛化能力。代码已开源。
原文摘要 · Abstract (English)
Graph Neural Networks (GNNs) are proficient in graph representation learning and achieve promising performance on versatile tasks such as node classification and link prediction. Usually, a comprehensive hyperparameter tuning is essential for fully unlocking GNN's top performance, especially for complicated tasks such as node classification on large graphs and long-range graphs. This is usually associated with high computational and time costs and careful design of appropriate search spaces. This work introduces a graph-conditioned latent diffusion framework (GNN-Diff) to generate high-performing GNNs based on the model checkpoints of sub-optimal hyperparameters selected by a light-tuning coarse search. We validate our method through 166 experiments across four graph tasks: node classification on small, large, and long-range graphs, as well as link prediction. Our experiments involve 10 classic and state-of-the-art target models and 20 publicly available datasets. The results consistently demonstrate that GNN-Diff: (1) boosts the performance of GNNs with efficient hyperparameter tuning; and (2) presents high stability and generalizability on unseen data across multiple generation runs. The code is available at https://github.com/lequanlin/GNN-Diff.
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