Graph contrastive learning is a general learning paradigm excelling at capturing invariant information from diverse perturbations in graphs. Recent works focus on exploring the structural rationale from graphs, thereby increasing the discriminability of the invariant information. However, such methods may incur in the mis-learning of graph models towards the interpretability of graphs, and thus the learned noisy and task-agnostic information interferes with the prediction of graphs. To this end, with the purpose of exploring the intrinsic rationale of graphs, we accordingly propose to capture the dimensional rationale from graphs, which has not received sufficient attention in the literature. The conducted exploratory experiments attest to the feasibility of the aforementioned roadmap. To elucidate the innate mechanism behind the performance improvement arising from the dimensional rationale, we rethink the dimensional rationale in graph contrastive learning from a causal perspective and further formalize the causality among the variables in the pre-training stage to build the corresponding structural causal model. On the basis of the understanding of the structural causal model, we propose the dimensional rationale-aware graph contrastive learning approach, which introduces a learnable dimensional rationale acquiring network and a redundancy reduction constraint. The learnable dimensional rationale acquiring network is updated by leveraging a bi-level meta-learning technique, and the redundancy reduction constraint disentangles the redundant features through a decorrelation process during learning. Empirically, compared with state-of-the-art methods, our method can yield significant performance boosts on various benchmarks with respect to discriminability and transferability. The code implementation of our method is available at https://github.com/ByronJi/DRGCL.

翻译：图对比学习是一种擅长从图的各种扰动中捕捉不变信息的一般学习范式。近期研究聚焦于探索图的结构性因果机制，以增强不变信息的可辨别性。然而，这类方法可能导致图模型对图可解释性的错误学习，从而使学习到的噪声和任务无关信息干扰图的预测。为此，为探索图的本质因果机制，我们提出从图中捕获维度级因果机制——这一方向此前未得到充分关注。开展的探索性实验验证了上述路径的可行性。为阐明维度因果机制带来性能提升的内在机理，我们从因果视角重新审视图对比学习中的维度因果机制，进一步形式化预训练阶段变量间的因果关系，构建对应的结构因果模型。基于对该结构因果模型的理解，我们提出维度因果感知图对比学习方法，引入可学习的维度因果获取网络与冗余归约约束。其中，可学习的维度因果获取网络通过双层元学习技术进行参数更新，而冗余归约约束通过学习过程中的去相关操作解耦冗余特征。实验表明，与当前最先进方法相比，我们的方法在多种基准测试中的可辨别性与可迁移性上均可实现显著性能提升。本方法代码实现见 https://github.com/ByronJi/DRGCL。