  Rationale is defined as a subset of input features that best explains or
supports the prediction by machine learning models. Rationale identification
has improved the generalizability and interpretability of neural networks on
vision and language data. In graph applications such as molecule and polymer
property prediction, identifying representative subgraph structures named as
graph rationales plays an essential role in the performance of graph neural
networks. Existing graph pooling and/or distribution intervention methods
suffer from lack of examples to learn to identify optimal graph rationales. In
this work, we introduce a new augmentation operation called environment
replacement that automatically creates virtual data examples to improve
rationale identification. We propose an efficient framework that performs
rationale-environment separation and representation learning on the real and
augmented examples in latent spaces to avoid the high complexity of explicit
graph decoding and encoding. Comparing against recent techniques, experiments
on seven molecular and four polymer real datasets demonstrate the effectiveness
and efficiency of the proposed augmentation-based graph rationalization
framework.
