Results 11 to 20 of about 533 (49)

Accurate graph classification via two-staged contrastive curriculum learning.

PLoS ONE
Given a graph dataset, how can we generate meaningful graph representations that maximize classification accuracy? Learning representative graph embeddings is important for solving various real-world graph-based tasks.
Sooyeon Shim, Junghun Kim, Kahyun Park, U Kang   +3 more
doaj   +1 more source

GCL-ALG: graph contrastive learning with adaptive learnable view generators [PDF]

PeerJ Computer Science
Data augmentation is a pivotal part of graph contrastive learning, which can mine implicit graph data information to improve the quality of representation learning.
Yafang Li, Jie Kang, Zhihua Chu, Baokai Zu   +3 more
doaj   +2 more sources

Graph Clustering with High-Order Contrastive Learning

Entropy, 2023
Graph clustering is a fundamental and challenging task in unsupervised learning. It has achieved great progress due to contrastive learning. However, we find that there are two problems that need to be addressed: (1) The augmentations in most graph ...
Wang Li, En Zhu, Siwei Wang, Xifeng Guo
doaj   +1 more source

CC-GNN: A Clustering Contrastive Learning Network for Graph Semi-Supervised Learning

IEEE Access
In graph modeling, scarcity of labeled data is a challenging issue. To address this issue, state-of-the-art graph models learn the representation of graph data via contrastive learning.
Peng Qin, Weifu Chen, Min Zhang, Defang Li, Guocan Feng   +4 more
doaj   +1 more source

Course Recommendation Model Based on Layer Dropout Graph Differential Contrastive Learning

IEEE Access
At present, the course recommendation model of graph collaborative filtering mainly uses bipartite graph modeling to obtain user-course cooperative relationship.
Yong Ouyang, Hao Long, Rong Gao, Jinghang Liu   +3 more
doaj   +1 more source

KAMPNet: multi-source medical knowledge augmented medication prediction network with multi-level graph contrastive learning

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, Haocheng Tang, Bo Jin, Yi Xu, Xiaopeng Wei   +4 more
doaj   +1 more source

Contrastive Graph Learning for Social Recommendation

Frontiers in Physics, 2022
Owing to the strength in learning representation of the high-order connectivity of graph neural networks (GNN), GNN-based collaborative filtering has been widely adopted in recommender systems.
Yongshuai Zhang, Yongshuai Zhang, Jiajin Huang, Jiajin Huang, Jiajin Huang, Jiajin Huang, Mi Li, Mi Li, Mi Li, Mi Li, Jian Yang, Jian Yang, Jian Yang, Jian Yang   +13 more
doaj   +1 more source

Line graph contrastive learning for node classification

Journal of King Saud University: Computer and Information Sciences
Existing graph contrastive learning methods often rely on differences in node features within subgraphs, lacking effective capture of the global structural information of the graph.
Mingyuan Li, Lei Meng, Zhonglin Ye, Yuzhi Xiao, Shujuan Cao, Haixing Zhao   +5 more
doaj   +1 more source

Improving Graph Collaborative Filtering from the Perspective of User–Item Interaction Directly Using Contrastive Learning

Mathematics
Graph contrastive learning has demonstrated significant superiority for collaborative filtering. These methods typically use augmentation technology to generate contrastive views, and then train graph neural networks with contrastive learning as an ...
Jifeng Dong, Yu Zhou, Shufeng Hao, Ding Feng, Haixia Zheng, Zhenhuan Xu   +5 more
doaj   +1 more source

GRE²-MDCL: Graph Representation Embedding Enhanced via Multidimensional Contrastive Learning

IEEE Access
Graph representation learning aims to preserve graph topology when mapping nodes to vector representations, enabling downstream tasks like node classification and community detection.
Quanjun Li, Weixuan Li, Xiya Zheng, Junhua Zhou, Wenming Zhong, Xuhang Chen, Chao Long   +6 more
doaj   +1 more source