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.

本文提出了一种名为 StableGNN 的基于因果关系的图神经网络框架，该框架从图数据中提取高级表示，并利用因果推断的区分能力帮助模型消除虚假相关性，具有良好的有效性、灵活性和解释性。