Graph contrastive learning - Open Access .click

Results 41 to 49 of about 533 (49)

SCoAMPS: Semi-Supervised Graph Contrastive Learning Based on Associative Memory Network and Pseudo-Label Similarity

Big Data Mining and Analytics
Graph data have extensive applications in various domains, including social networks, biological reaction networks, and molecular structures. Graph classification aims to predict the properties of entire graphs, playing a crucial role in many downstream ...
Zaigang Gong +5 more
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Prototypical Graph Contrastive Learning for Recommendation

Applied Sciences
Data sparsity caused by limited interactions makes it challenging for recommendation to accurately capture user preferences. Contrastive learning effectively alleviates this issue by enriching embedding information through the learning of diverse ...
Tao Wei, Changchun Yang, Yanqi Zheng
doaj +1 more source

Contrastive learning on high-order noisy graphs for collaborative recommendation

Scientific Reports
The graph-based collaborative filtering method has shown significant application value in recommendation systems, as it models user-item preferences by constructing a user-item interaction graph.
Jiahao Wang +3 more
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Multi-granularity contrastive learning model for next POI recommendation

Frontiers in Neurorobotics
Next Point-of-Interest (POI) recommendation aims to predict the next POI for users from their historical activities. Existing methods typically rely on location-level POI check-in trajectories to explore user sequential transition patterns, which suffer ...
Yunfeng Zhu, Shuchun Yao, Xun Sun
doaj +1 more source

Dual intent view contrastive learning for knowledge aware recommender systems

Scientific Reports
Knowledge-aware recommendation systems often face challenges owing to sparse supervision signals and redundant entity relations, which can diminish the advantages of utilizing knowledge graphs for enhancing recommendation performance.
Jianhua Guo +4 more
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MPHGCL-DDI: Meta-Path-Based Heterogeneous Graph Contrastive Learning for Drug-Drug Interaction Prediction

Molecules
The 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
doaj +1 more source

Spectro-Image Analysis with Vision Graph Neural Networks and Contrastive Learning for Parkinson’s Disease Detection

Journal of Imaging
This study presents a novel framework that integrates Vision Graph Neural Networks (ViGs) with supervised contrastive learning for enhanced spectro-temporal image analysis of speech signals in Parkinson’s disease (PD) detection. The approach introduces a
Nuwan Madusanka +5 more
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Graph Contrastive-and-Reconstructive Hashing for Unsupervised Cross-Modal Retrieval

Data Science and Engineering
Hashing-based unsupervised cross-modal retrieval has gained significant attention in the big data management community due to its low storage overhead and rapid retrieval speed. However, current methods often lack effective alignment strategies to reduce
Rukai Wei +4 more
doaj +1 more source

A dual contrastive learning-based graph convolutional network with syntax label enhancement for aspect-based sentiment classification

Frontiers in Physics
Introduction: Aspect-based sentiment classification is a fine-grained sentiment classification task. State-of-the-art approaches in this field leverage graph neural networks to integrate sentence syntax dependency.
Yuyan Huang +7 more
doaj +1 more source

contrastive learning
graph neural networks
self-supervised learning

graph neural network
graph representation learning
medicine

recommendation
collaborative filtering