Graph Rationalization with Environment-based Augmentations

TL;DR

This paper introduces environment replacement augmentation to enhance graph rationale identification, improving interpretability and performance in molecular and polymer property prediction tasks.

Contribution

It proposes a novel environment replacement augmentation method and an efficient latent space framework for better graph rationale extraction.

Findings

Outperforms recent methods on multiple molecular and polymer datasets.

Improves interpretability of graph neural networks.

Enhances rationale identification accuracy and efficiency.

Abstract

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.