Results 1 to 10 of about 533 (49)
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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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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Community-CL: An Enhanced Community Detection Algorithm Based on Contrastive Learning
Entropy, 2023 Graph contrastive learning (GCL) has gained considerable attention as a self-supervised learning technique that has been successfully employed in various applications, such as node classification, node clustering, and link prediction.
Zhaoci Huang, Wenzhe Xu, Xinjian Zhuo
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Asymmetric Graph Contrastive Learning
Mathematics, 2023 Learning effective graph representations in an unsupervised manner is a popular research topic in graph data analysis. Recently, contrastive learning has shown its success in unsupervised graph representation learning.
Xinglong Chang +4 more
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IEEE Access, 2023 Self-supervised learning has been shown to be effective in various fields, proving its usefulness in contrastive learning. Recently, graph contrastive learning has shown state-of-the-art performance in the recommendation task.
Sanghun Kim, Hyeryung Jang
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Multi-view Graph Clustering Algorithm Based on Dual Contrastive Learning and Hard Sample Mining [PDF]
Jisuanji gongchengAs a key research direction in the field of graph mining, graph clustering aims to discover substructures or node groups with similarities from graph data and classify them into the same cluster.
QIAN Lifeng, LI Jing, ZOU Xuxi, CHEN Yu, GU Yalin, WEI Xunhu
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Subgraph Adaptive Structure-Aware Graph Contrastive Learning
Mathematics, 2022 Graph contrastive learning (GCL) has been subject to more attention and been widely applied to numerous graph learning tasks such as node classification and link prediction. Although it has achieved great success and even performed better than supervised
Zhikui Chen +4 more
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XSGCL: A Lightweight Graph Contrastive Learning Framework for Recommendation [PDF]
Jisuanji gongchengTraditional recommendation models based on contrastive learning first perform data augmentation on the original interaction graph and then strive to improve the consistency of representations encoded from different views.
ZHANG Zhen, YOU Lan, PENG Qingxi, JIN Hong, ZENG Haoqiu, XIA Yuchun
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