Graph Neural Networks (GNNs) perform effectively when training and testing graphs are drawn from the same distribution, but struggle to generalize well in the face of distribution shifts. To address this issue, existing mainstreaming graph rationalization methods first identify rationale and environment subgraphs from input graphs, and then diversify training distributions by augmenting the environment subgraphs. However, these methods merely combine the learned rationale subgraphs with environment subgraphs in the representation space to produce augmentation samples, failing to produce sufficiently diverse distributions. Thus, in this paper, we propose to achieve an effective Graph Rationalization by Boosting Environmental diversity, a GRBE approach that generates the augmented samples in the original graph space to improve the diversity of the environment subgraph. Firstly, to ensure the effectiveness of augmentation samples, we propose a precise rationale subgraph extraction strategy in GRBE to refine the rationale subgraph learning process in the original graph space. Secondly, to ensure the diversity of augmented samples, we propose an environment diversity augmentation strategy in GRBE that mixes the environment subgraphs of different graphs in the original graph space and then combines the new environment subgraphs with rationale subgraphs to generate augmented graphs. The average improvements of 7.65% and 6.11% in rationalization and classification performance on benchmark datasets demonstrate the superiority of GRBE over state-of-the-art approaches.

翻译：图神经网络（GNNs）在训练图与测试图来自相同分布时表现良好，但在面对分布偏移时泛化能力不足。为解决此问题，现有主流图合理化方法首先从输入图中识别出合理化子图与环境子图，然后通过增强环境子图来多样化训练分布。然而，这些方法仅将学习到的合理化子图与环境子图在表示空间中进行组合以生成增强样本，未能产生足够多样化的分布。因此，本文提出通过提升环境多样性来实现有效的图合理化，即GRBE方法，该方法在原始图空间中生成增强样本以提高环境子图的多样性。首先，为确保增强样本的有效性，我们在GRBE中提出了一种精确的合理化子图提取策略，以优化原始图空间中的合理化子图学习过程。其次，为确保增强样本的多样性，我们在GRBE中提出了一种环境多样性增强策略，该策略在原始图空间中混合不同图的环境子图，然后将新的环境子图与合理化子图结合以生成增强图。在基准数据集上，合理化与分类性能分别平均提升7.65%和6.11%，这证明了GRBE相较于现有先进方法的优越性。