Learning to Distill Graph Neural Networks.

Graph Neural Networks (GNNs) can effectively capture both the topology and attribute information of a graph, and have been extensively studied in many domains. Recently, there is an emerging trend that equips GNNs with knowledge distillation for better efficiency or effectiveness. However, to the best of our knowledge, existing knowledge distillation methods applied on GNNs all employed predefined distillation processes, which are controlled by several hyper-parameters without any supervision from the performance of distilled models. Such isolation between distillation and evaluation would lead to suboptimal results. In this work, we aim to propose a general knowledge distillation framework that can be applied on any pretrained GNN models to further improve their performance. To address the isolation problem, we propose to parameterize and learn distillation processes suitable for distilling GNNs. Specifically, instead of introducing a unified temperature hyper-parameter as most previous work did, we will learn node-specific distillation temperatures towards better performance of distilled models. We first parameterize each node's temperature by a function of its neighborhood's encodings and predictions, and then design a novel iterative learning process for model distilling and temperature learning. We also introduce a scalable variant of our method to accelerate model training. Experimental results on five benchmark datasets show that our proposed framework can be applied on five popular GNN models and consistently improve their prediction accuracies with 3.12% relative enhancement on average. Besides, the scalable variant enables 8 times faster training speed at the cost of 1% prediction accuracy.