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.

在图神经网络的端到端图表示学习中，由于图数据中复杂的因果关系和规则使得模型准确捕捉真实数据关系变得困难。本文提出一种缓解策略，即将与图数据对应的规则或关系直接整合到模型中。然而，在图表示学习领域中，图数据的固有复杂性阻碍了建立一个包含整个数据集的普遍规则或关系的全面因果结构的推导。相反，只有专门的因果结构，揭示了约束子集内的特定因果关系。基于经验见解，我们观察到 GNN 模型在训练过程中收敛于这些专门的因果结构。因此，我们认为引入这些特定的因果结构有助于 GNN 模型的训练。在这一前提下，我们提出了一种新方法，使 GNN 模型能够从这些专门的因果结构中获得洞察力，从而提高整体性能。我们的方法特别从这些特定的因果结构的模型表示中提取因果知识，并引入交换干预来优化学习过程。理论分析证实了我们提出方法的有效性。此外，实证实验在不同数据集上一致地展示了显著的性能改进。