Results 141 to 150 of about 30,384 (158)

Unsupervised Structure-Adaptive Graph Contrastive Learning

IEEE Transactions on Neural Networks and Learning Systems
Graph contrastive learning, which to date has always been guided by node features and fixed-intrinsic structures, has become a prominent technique for unsupervised graph representation learning through contrasting positive-negative counterparts. However, the fixed-intrinsic structure cannot represent the potential relationships beneficial for models ...
Han Zhao, Xu Yang, Cheng Deng, Dacheng Tao   +3 more
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Multi-relational graph contrastive learning with learnable graph augmentation

Neural Networks
Multi-relational graph learning aims to embed entities and relations in knowledge graphs into low-dimensional representations, which has been successfully applied to various multi-relationship prediction tasks, such as information retrieval, question answering, and etc. Recently, contrastive learning has shown remarkable performance in multi-relational
Xian Mo, Jun Pang, Binyuan Wan, Rui Tang, Hao Liu, Shuyu Jiang   +5 more
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AGCL: Adaptive Graph Contrastive Learning for graph representation learning

Neurocomputing, 2023
Jiajun Yu, Adele Lu Jia
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Graph Contrastive Learning With Adaptive Proximity-Based Graph Augmentation

IEEE Transactions on Neural Networks and Learning Systems
Graph neural networks (GNNs) have been successful in a variety of graph-based applications. Recently, it is shown that capturing long-range relationships between nodes helps improve the performance of GNNs. The phenomenon is mostly confirmed in a supervised learning setting.
Wei Zhuo, Guang Tan
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Universal Graph Self-Contrastive Learning

Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence
As a pivotal architecture in Self-Supervised Learning (SSL), Graph Contrastive Learning (GCL) has demonstrated substantial application value in scenarios with limited labeled nodes (samples). However, existing GCLs encounter critical issues in the graph augmentation and positive and negative sampling stemming from the lack of explicit supervision ...
Liang Yang, Yukun Cai, Hui Ning, Jiaming Zhuo, Di Jin, Ziyi Ma, Yuanfang Guo, Chuan Wang, Zhen Wang   +8 more
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Graph Joint Representation Clustering via Penalized Graph Contrastive Learning

IEEE Transactions on Neural Networks and Learning Systems
Graph clustering based on graph contrastive learning (GCL) is one of the dominant paradigms in the current graph clustering research field. However, those GCL-based methods often yield false negative samples, which can distort the learned representations and limit clustering performance.
Zihua Zhao, Rong Wang, Zheng Wang, Feiping Nie, Xuelong Li   +4 more
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Hierarchical Graph Contrastive Learning

2023
Hao Yan, Senzhang Wang, Jun Yin, Chaozhuo Li, Junxing Zhu, Jianxin Wang   +5 more
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Graph Contrastive Learning with Learnable Graph Augmentation

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023
Pu, Xinyan, Zhang, Ke, Shu, Huazhong, Coatrieux, Jean-Louis, Kong, Youyong   +4 more
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Graph Contrastive Learning with Graph Info-Min

Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, 2023
En Meng, Yong Liu
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Graph Contrastive Learning with Line Graph Augmentation

2023
Yan Wang, Hai Wang, Rui Cao, Tao Liu, Xinyi Zhang   +4 more
openaire   +1 more source