Hierarchical Topology Isomorphism Expertise Embedded Graph Contrastive Learning
DOI:
https://doi.org/10.1609/aaai.v38i12.29255Keywords:
ML: Graph-based Machine Learning, ML: Unsupervised & Self-Supervised LearningAbstract
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.Downloads
Published
2024-03-24
How to Cite
Li, J., Jin, Y., Gao, H., Qiang, W., Zheng, C., & Sun, F. (2024). Hierarchical Topology Isomorphism Expertise Embedded Graph Contrastive Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 38(12), 13518-13527. https://doi.org/10.1609/aaai.v38i12.29255
Issue
Section
AAAI Technical Track on Machine Learning III