Graph contrastive learning (GCL) aims to align the positive features while
differentiating the negative features in the latent space by minimizing a
pair-wise contrastive loss. As the embodiment of an outstanding discriminative
unsupervised graph representation learning approach, GCL achieves impressive
successes in various graph benchmarks. However, such an approach falls short of
recognizing the topology isomorphism of graphs, resulting in that graphs with
relatively homogeneous node features cannot be sufficiently discriminated. By
revisiting classic graph topology recognition works, we disclose that the
corresponding expertise intuitively complements GCL methods. To this end, we
propose a novel hierarchical topology isomorphism expertise embedded graph
contrastive learning, which introduces knowledge distillations to empower GCL
models to learn the hierarchical topology isomorphism expertise, including the
graph-tier and subgraph-tier. On top of this, the proposed method holds the
feature of plug-and-play, and we empirically demonstrate that the proposed
method is universal to multiple state-of-the-art GCL models. The solid
theoretical analyses are further provided to prove that compared with
conventional GCL methods, our method acquires the tighter upper bound of Bayes
classification error. We conduct extensive experiments on real-world benchmarks
to exhibit the performance superiority of our method over candidate GCL
methods, e.g., for the real-world graph representation learning experiments,
the proposed method beats the state-of-the-art method by 0.23\% on unsupervised
representation learning setting, 0.43\% on transfer learning setting. Our code
is available at this https URL

通过引入知识蒸馏，我们提出了一种新颖的层次化拓扑同构专家嵌入图对比学习方法，用于增强 GCL 模型学习层次化拓扑同构专业知识，包括图层和子图层。与传统的 GCL 方法相比，我们的方法在贝叶斯分类错误上获得更紧的上界，并在真实世界基准测试中表现出了超越候选 GCL 方法的性能优势。