Graph Neural Networks (GNNs) are proposed without considering the agnostic distribution shifts between training and testing graphs, inducing the degeneration of the generalization ability of GNNs on Out-Of-Distribution (OOD) settings. The fundamental reason for such degeneration is that most GNNs are developed based on the I.I.D hypothesis. In such a setting, GNNs tend to exploit subtle statistical correlations existing in the training set for predictions, even though it is a spurious correlation. However, such spurious correlations may change in testing environments, leading to the failure of GNNs. Therefore, eliminating the impact of spurious correlations is crucial for stable GNNs. To this end, we propose a general causal representation framework, called StableGNN. The main idea is to extract high-level representations from graph data first and resort to the distinguishing ability of causal inference to help the model get rid of spurious correlations. Particularly, we exploit a graph pooling layer to extract subgraph-based representations as high-level representations. Furthermore, we propose a causal variable distinguishing regularizer to correct the biased training distribution. Hence, GNNs would concentrate more on the stable correlations. Extensive experiments on both synthetic and real-world OOD graph datasets well verify the effectiveness, flexibility and interpretability of the proposed framework.

翻译：图神经网络（GNN）在提出时未考虑训练图与测试图之间不可知的分布偏移，导致GNN在分布外（OOD）场景下的泛化能力退化。这种退化的根本原因在于大多数GNN基于独立同分布假设构建。在此假设下，GNN倾向于利用训练集中存在的微妙统计相关性（即使其属于虚假相关性）进行预测。然而这类虚假相关性在测试环境中可能发生变化，从而导致GNN失效。因此，消除虚假相关性的影响对于构建稳定的GNN至关重要。为此，我们提出一个通用因果表示框架StableGNN。其核心思想是：首先从图数据中提取高层表征，继而借助因果推理的区分能力帮助模型摆脱虚假相关性。具体而言，我们利用图池化层提取基于子图的表征作为高层表征，并进一步提出因果变量区分正则化器来校正有偏训练分布，从而使GNN更关注稳定相关性。在合成和真实OOD图数据集上的大量实验充分验证了所提框架的有效性、灵活性和可解释性。