Graph contrastive learning with node-level accurate difference [PDF]
Fundamental ResearchGraph contrastive learning (GCL) has attracted extensive research interest due to its powerful ability to capture latent structural and semantic information of graphs in a self-supervised manner.
Pengfei Jiao +5 more
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A Good View for Graph Contrastive Learning [PDF]
EntropyDue to the success observed in deep neural networks with contrastive learning, there has been a notable surge in research interest in graph contrastive learning, primarily attributed to its superior performance in graphs with limited labeled data. Within
Xueyuan Chen, Shangzhe Li
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GraphGIM: rethinking molecular graph contrastive learning via geometry image modeling [PDF]
BMC BiologyBackground Learning molecular representations is crucial for accurate drug discovery. Using graphs to represent molecules is a popular solution, and many researchers have used contrastive learning to improve the generalization of molecular graph ...
Chaoyi Li +6 more
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KAMPNet: multi-source medical knowledge augmented medication prediction network with multi-level graph contrastive learning [PDF]
BMC Medical Informatics and Decision Making, 2023 Backgrounds Predicting medications is a crucial task in intelligent healthcare systems, aiding doctors in making informed decisions based on electronic medical records (EMR). However, medication prediction faces challenges due to complex relations within
Yang An +4 more
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MPHGCL-DDI: Meta-Path-Based Heterogeneous Graph Contrastive Learning for Drug-Drug Interaction Prediction [PDF]
MoleculesThe combinatorial therapy with multiple drugs may lead to unexpected drug-drug interactions (DDIs) and result in adverse reactions to patients. Predicting DDI events can mitigate the potential risks of combinatorial therapy and enhance drug safety.
Baofang Hu, Zhenmei Yu, Mingke Li
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Self-supervised Dynamic Graph Representation Learning Approach Based on Contrastive Prediction [PDF]
Jisuanji kexue, 2023 In recent years,graph self-supervised learning represented by graph contrastive learning has become a hot research to-pic in the field of graph learning.This learning paradigm does not depend on node labels and has good generalization ability.However ...
JIANG Linpu, CHEN Kejia
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Signal Contrastive Enhanced Graph Collaborative Filtering for Recommendation
Data Science and Engineering, 2023 Graph collaborative filtering methods have shown great performance improvements compared with deep neural network-based models. However, these methods suffer from data sparsity and data noise problems.
Zhi-Yuan Li +3 more
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SC-FGCL: Self-Adaptive Cluster-Based Federal Graph Contrastive Learning
IEEE Open Journal of the Computer Society, 2023 As a self-supervised learning method, the graph contrastive learning achieve admirable performance in graph pre-training tasks, and can be fine-tuned for multiple downstream tasks such as protein structure prediction, social recommendation, etc.
Tingqi Wang +4 more
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Structure-Augmented Text Representation Learning for Efficient Knowledge Graph Completion [PDF]
, 2021 Human-curated knowledge graphs provide critical supportive information to various natural language processing tasks, but these graphs are usually incomplete, urging auto-completion of them.
Bo Wang +5 more
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CAAI Transactions on Intelligence Technology, 2023 Contrastive self‐supervised representation learning on attributed graph networks with Graph Neural Networks has attracted considerable research interest recently. However, there are still two challenges.
Beibei Han +3 more
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