  The recent contrastive learning methods, due to their effectiveness in
representation learning, have been widely applied to modeling graph data.
Random perturbation is widely used to build contrastive views for graph data,
which however, could accidentally break graph structures and lead to suboptimal
performance. In addition, graph data is usually highly abstract, so it is hard
to extract intuitive meanings and design more informed augmentation schemes.
Effective representations should preserve key characteristics in data and
abandon superfluous information. In this paper, we propose ENGAGE (ExplaNation
Guided data AuGmEntation), where explanation guides the contrastive
augmentation process to preserve the key parts in graphs and explore removing
superfluous information. Specifically, we design an efficient unsupervised
explanation method called smoothed activation map as the indicator of node
importance in representation learning. Then, we design two data augmentation
schemes on graphs for perturbing structural and feature information,
respectively. We also provide justification for the proposed method in the
framework of information theories. Experiments of both graph-level and
node-level tasks, on various model architectures and on different real-world
graphs, are conducted to demonstrate the effectiveness and flexibility of
ENGAGE. The code of ENGAGE can be found: https://github.com/sycny/ENGAGE.
