When engaging in end-to-end graph representation learning with Graph Neural Networks (GNNs), the intricate causal relationships and rules inherent in graph data pose a formidable challenge for the model in accurately capturing authentic data relationships. A proposed mitigating strategy involves the direct integration of rules or relationships corresponding to the graph data into the model. However, within the domain of graph representation learning, the inherent complexity of graph data obstructs the derivation of a comprehensive causal structure that encapsulates universal rules or relationships governing the entire dataset. Instead, only specialized diminutive causal structures, delineating specific causal relationships within constrained subsets of graph data, emerge as discernible. Motivated by empirical insights, it is observed that GNN models exhibit a tendency to converge towards such specialized causal structures during the training process. Consequently, we posit that the introduction of these specific causal structures is advantageous for the training of GNN models. Building upon this proposition, we introduce a novel method that enables GNN models to glean insights from these specialized diminutive causal structures, thereby enhancing overall performance. Our method specifically extracts causal knowledge from the model representation of these diminutive causal structures and incorporates interchange intervention to optimize the learning process. Theoretical analysis serves to corroborate the efficacy of our proposed method. Furthermore, empirical experiments consistently demonstrate significant performance improvements across diverse datasets.

翻译：在使用图神经网络（GNN）进行端到端的图表示学习时，图数据中固有的复杂因果关系与规则对模型准确捕捉真实数据关系构成了严峻挑战。一种被提出的缓解策略是将与图数据对应的规则或关系直接整合到模型中。然而，在图表示学习领域，图数据固有的复杂性阻碍了能够概括整个数据集通用规则或关系的全面因果结构的推导。相反，只有那些描述图数据受限子集内特定因果关系的、专门的微小因果结构是可辨识的。基于实证观察的启发，我们注意到GNN模型在训练过程中倾向于收敛到此类专门的因果结构。因此，我们假设引入这些特定的因果结构有利于GNN模型的训练。基于这一假设，我们提出了一种新方法，使GNN模型能够从这些专门的微小因果结构中汲取洞见，从而提升整体性能。我们的方法专门从这些微小因果结构的模型表示中提取因果知识，并引入交换干预以优化学习过程。理论分析证实了我们所提方法的有效性。此外，实证实验在多个数据集上一致地展示了显著的性能提升。