Models for natural language and images benefit from data scaling behavior:
the more data fed into the model, the better they perform. This 'better with
more' phenomenon enables the effectiveness of large-scale pre-training on vast
amounts of data. However, current graph pre-training methods struggle to scale
up data due to heterogeneity across graphs. To achieve effective data scaling,
we aim to develop a general model that is able to capture diverse data patterns
of graphs and can be utilized to adaptively help the downstream tasks. To this
end, we propose UniAug, a universal graph structure augmentor built on a
diffusion model. We first pre-train a discrete diffusion model on thousands of
graphs across domains to learn the graph structural patterns. In the downstream
phase, we provide adaptive enhancement by conducting graph structure
augmentation with the help of the pre-trained diffusion model via guided
generation. By leveraging the pre-trained diffusion model for structure
augmentation, we consistently achieve performance improvements across various
downstream tasks in a plug-and-play manner. To the best of our knowledge, this
study represents the first demonstration of a data-scaling graph structure
augmentor on graphs across domains.

我们提出了 UniAug，这是一个建立在扩散模型之上的通用图结构增强器，通过预训练的扩散模型进行结构增强，在不同领域的图上实现了性能的持续改进，并在图数据扩展领域展示了首次示范。