Hierarchical Topology Isomorphism Expertise Embedded Graph Contrastive Learning
Jiangmeng Li, Yifan Jin, Hang Gao, Wenwen Qiang, Changwen Zheng,, Fuchun Sun

TL;DR
This paper introduces a hierarchical topology isomorphism expertise into graph contrastive learning, enhancing its ability to recognize graph topology and improving performance on real-world benchmarks.
Contribution
It proposes a novel plug-and-play method that incorporates hierarchical topology expertise into GCL via knowledge distillation, applicable to multiple state-of-the-art models.
Findings
Outperforms existing GCL methods on real-world benchmarks.
Achieves 0.23% and 0.43% improvements in unsupervised and transfer learning.
Provides theoretical proof of tighter Bayes error bounds.
Abstract
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…
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Taxonomy
TopicsAdvanced Graph Neural Networks · Recommender Systems and Techniques
MethodsContrastive Learning · ALIGN
