  Despite the Graph Neural Networks' (GNNs) proficiency in analyzing graph
data, achieving high-accuracy and interpretable predictions remains
challenging. Existing GNN interpreters typically provide post-hoc explanations
disjointed from GNNs' predictions, resulting in misrepresentations.
Self-explainable GNNs offer built-in explanations during the training process.
However, they cannot exploit the explanatory outcomes to augment prediction
performance, and they fail to provide high-quality explanations of node
features and require additional processes to generate explainable subgraphs,
which is costly. To address the aforementioned limitations, we propose a
self-explained and self-supervised graph neural network (SES) to bridge the gap
between explainability and prediction. SES comprises two processes: explainable
training and enhanced predictive learning. During explainable training, SES
employs a global mask generator co-trained with a graph encoder and directly
produces crucial structure and feature masks, reducing time consumption and
providing node feature and subgraph explanations. In the enhanced predictive
learning phase, mask-based positive-negative pairs are constructed utilizing
the explanations to compute a triplet loss and enhance the node representations
by contrastive learning.
