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Towards Graph Contrastive Learning: A Survey and Beyond
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In recent years, deep learning on graphs has achieved remarkable success in various domains. However, the reliance on annotated graph data remains a significant bottleneck due to its prohibitive cost and time-intensive nature. To address this challenge, self-supervised learning (SSL) on graphs has gained increasing attention and has made significant progress. SSL enables machine learning models to produce informative representations from unlabeled graph data, reducing the reliance on expensive labeled data. While SSL on graphs has witnessed widespread adoption, one critical component, Graph Contrastive Learning (GCL), has not been thoroughly investigated in the existing literature. Thus, this survey aims to fill this gap by offering a dedicated survey on GCL. We provide a comprehensive overview of the fundamental principles of GCL, including data augmentation strategies, contrastive modes, and contrastive optimization objectives. Furthermore, we explore the extensions of GCL to other aspects of data-efficient graph learning, such as weakly supervised learning, transfer learning, and related scenarios. We also discuss practical applications spanning domains such as drug discovery, genomics analysis, recommender systems, and finally outline the challenges and potential future directions in this field.
Forward citations
Cited by 5 Pith papers
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Graph Positional Autoencoders as Self-supervised Learners
A dual-path graph autoencoder that reconstructs node features and Laplacian-eigenvector distances reports strong self-supervised results on heterophilic and molecular benchmarks, with some overstatement in the margins...
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DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining
A self-supervised graph encoder for device-level circuits improves prediction of circuit similarity, delays, and op-amp performance across analog and digital designs.
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Str-GCL: Structural Commonsense Driven Graph Contrastive Learning
Injecting hand-designed structural rules (low-neighbor-degree and local-global similarity) via representation alignment improves graph contrastive learning accuracy on six homophilic benchmarks.
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Subgraph Gaussian Embedding Contrast for Self-Supervised Graph Representation Learning
SubGEC adds a KL-regularized Gaussian embedding to subgraph contrastive learning with Wasserstein and Gromov-Wasserstein distances, improving node classification on several heterophilic graphs.
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HGCL: Hierarchical Graph Contrastive Learning for User-Item Recommendation
Adding a t-SNE-based hierarchical item clustering step to graph contrastive learning yields small accuracy gains on three benchmark recommendation datasets.
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