Many domains in machine learning have datasets with a large number of related but different tasks. Those domains are challenging because task-specific labels are often scarce and test examples can be distributionally different from examples seen during training. An effective solution to these challenges is to pre-train a model on related tasks where data is abundant, and then fine-tune it on a downstream task of interest. While pre-training has been effective for improving many language and vision domains, pre-training on graph datasets remains an open question. Here, we develop a strategy for pre-training Graph Neural Networks (GNNs). Crucial to the success of our strategy is to pre-train an expressive GNN at the level of individual nodes as well as entire graphs. We systematically study different pre-training strategies on multiple datasets and find that when ad-hoc strategies are applied, pre-trained GNNs often exhibit negative transfer and perform worse than non-pretrained GNNs on many downstream tasks. In contrast, our proposed strategy is effective and avoids negative transfer across downstream tasks, leading up to 11.7% absolute improvements in ROC-AUC over non-pre-trained models and achieving state of the art performance.
The paper proposes pre-training strategies (PT) for graph neural networks (GNN) from both node and graph levels. Two new large-scale pre-training datasets are created and extensive experiments are conducted to demonstrate the benefits of PT upon different GNN architectures. I am relative positive for this work. Detail review of different aspects and questions are as follows. 
Novelty: As far as I know, this work is among the earliest works to think about GNN pre-training. The most similar paper at the same period is [Z Hu, arXiv:1905.13728]. I read both papers and found they have similar idea about PT although they have different designs. This paper leverages graph structure (e.g., context neighbors) and supervised labels/attributes (e.g., node attributes, graph labels) for PT. These strategies are not surprising for me and the novelty is incremental. 
Experiment: The experiments are overall good. The authors created two new large scale pre-training graph datasets. Experimental results of different GNN architectures w/o different PT for different tasks are provided. Comparing to non-pretraining GNN, the improvements are significant for most cases. 
Writing: The writing is good and easy to follow. 
Questions: I would like to see more discussion about difference between this work and [Z Hu, arXiv:1905.13728]. Comparing to the other work, what are strengths of this work? In addition, have the authors compared the performances of their work and [Z Hu, arXiv:1905.13728] using the same data?