  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 https://github.com/jyf123/HTML.
