  Self-supervision as an emerging technique has been employed to train
convolutional neural networks (CNNs) for more transferrable, generalizable, and
robust representation learning of images. Its introduction to graph
convolutional networks (GCNs) operating on graph data is however rarely
explored. In this study, we report the first systematic exploration and
assessment of incorporating self-supervision into GCNs. We first elaborate
three mechanisms to incorporate self-supervision into GCNs, analyze the
limitations of pretraining & finetuning and self-training, and proceed to focus
on multi-task learning. Moreover, we propose to investigate three novel
self-supervised learning tasks for GCNs with theoretical rationales and
numerical comparisons. Lastly, we further integrate multi-task self-supervision
into graph adversarial training. Our results show that, with properly designed
task forms and incorporation mechanisms, self-supervision benefits GCNs in
gaining more generalizability and robustness. Our codes are available at
https://github.com/Shen-Lab/SS-GCNs.
