Diffusion-Assisted Distillation for Self-Supervised Graph Representation Learning with MLPs

Abstract--For large-scale applications, there is growing interest in replacing Graph Neural Networks (GNNs) with lightweight Multi-Layer Perceptrons (MLPs) via knowledge distillation. However, distilling GNNs for self-supervised graph representation learning into MLPs is more challenging. This is because the performance of self-supervised learning is more related to the model's inductive bias than supervised learning. This motivates us to design a new distillation method to bridge a huge capacity gap between GNNs and MLPs in self-supervised graph representation learning. In this paper, we propose Diffusion-Assisted Distillation for Self-supervised Graph representation learning with MLPs (DAD-SGM). The proposed method employs a denoising diffusion model as a teacher assistant to better distill the knowledge from the teacher GNN into the student MLP . This approach enhances the generalizability and robustness of MLPs in self-supervised graph representation learning. Extensive experiments demonstrate that DAD-SGM effectively distills the knowledge of self-supervised GNNs compared to state-of-the-art GNN-to-MLP distillation methods. Impact Statement--This paper presents Diffusion-Assisted Distillation for Self-supervised Graph representation learning with MLPs (DAD-SGM), a novel framework that addresses the performance gap between GNNs and MLPs in self-supervised graph learning. Our approach first trains an assistant denoising diffusion model that learns to predict noise from noisy outputs of the GNN teacher .