  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.
