.png){kind=icon}
返回
SGCL: Semi-supervised Graph Contrastive Learning with confidence propagation algorithm for node classification
.png){kind=icon}
PRE
AI
DOI：10.1016/j.knosys.2024.112271.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Semi-Supervised Graph Learning (SSGL) aims to predict massive unknown labels based on a subset of known labels within a graph. Recently, graph neural network, one of the most popular SSGL approaches, has garnered considerable research interest and achieved remarkable progress. However, many of these methods perform unsatisfactorily with limited labeled data. Graph contrastive learning (GCL), which utilizes unlabeled data to generate supervision, partially addresses this issue but does not fully exploit label information. To address this challenge, we propose SSGL algorithm, Semi-supervised Graph Contrastive Learning with Confidence Propagation Algorithm (SGCL). SGCL comprises two stages of contrastive learning. In the first stage, we employ unsupervised contrastive learning to initialize the model with graph augmentation. In the second stage, in order to fully leverage known labels and graph structure, we incorporate supervised contrastive learning which utilizes supervision signals obtained from confidence propagation algorithm. By combining supervised contrastive learning and unsupervised contrastive learning, the embedding quality and the classification accuracy can be further enhanced. At last, comprehensive experiments demonstrate that SGCL outperforms the best baseline method by an average of 2.23% across six datasets, highlighting the effectiveness of our approach.
Keyword:
Graph Contrastive Learning
Label Propagation Algorithm
Node classification
Pseudo label
期刊
K
IF:
7.6
论文数:
1.2W
被引数:
4.5W
机构
引用论文
Can CD34 discriminate between benign and malignant hepatocytic lesions in fine-needle aspirates and thin core biopsies?.png)
Cancer
OAAI
CancerIF0
OAAI分享收藏
Insurance activity and economic performance: Fresh evidence from asymmetric panel causality testsOAAI
OAAI分享收藏Passive signal propagation and membrane properties in median photoreceptors of the giant barnacleOAAI
OAAI分享收藏A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications图嵌入的综合调查: 问题，技术和应用OAAI
OAAI分享收藏
查看更多内容