Graph neural networks (GNNs) have received extensive attention from researchers due to their excellent performance on various graph learning tasks such as social analysis [24, 17, 29], drug discovery [12, 25], traffic forecasting [18, 3, 6], recommendation system [38, 32] and computer vision[39,4].

Diffusion models have recently dominated image synthesis and other related generative tasks. However, the iterative denoising process is expensive in computations at inference time, making diffusion models less practical for low-latency and scalable real-world applications.

An instance feature extractor is shared between theteacher andthestudent tofurther enhance theknowledge exchange between them. In addition, we propose ahard positiveinstance mining strategy based on the output of the student network to force the teacher network to keep mininghardpositiveinstances.