Results 31 to 40 of about 6,810,610 (247)

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

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

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

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

Microbiome‐blood–brain barrier interactions in aging — mechanisms and therapeutic potential

FEBS Letters, EarlyView.
Aging reshapes the gut microbiome (↓SCFA‐producing commensals; ↑pro‐inflammatory outputs), shifting circulating metabolites (↓SCFAs; ↑LPS, ↑TMAO, ↑PAA) that act at the BBB to increase nonspecific transcytosis, alter transport, and promote astrocyte reactivity, heightening brain vulnerability.
Daniel Cuervo‐Zanatta, Hari Baskar Balasubramanian, Amir Pasokh, Marietta Zille   +3 more
wiley   +1 more source

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

Knowledge-aware contrastive heterogeneous molecular graph learning. [PDF]

Molecular representation learning is pivotal in predicting molecular properties and advancing drug design. Traditional methodologies, which predominantly rely on homogeneous graph encoding, are limited by their inability to integrate external knowledge ...
Jia Wu, Chen, Mukun, Hu, Wenbin, Wu, Jia, Bo Du, Shirui Pan, Fu Lin, Pan, Shirui, Gong, Xiuwen, Xiuwen Gong, Wenbin Hu, Du, Bo, Lin, Fu, Mukun Chen   +13 more
core   +1 more source

Multi-scale contrastive siamese networks for self-supervised graph representation learning [PDF]

, 2021
Graph representation learning plays a vital role in processing graph-structured data. However, prior arts on graph representation learning heavily rely on labeling information. To overcome this problem, inspired by the recent success of graph contrastive
Chen Gong, Zhou, C, Zheng, Yizhen, Gong, Chen, Li, Yuan-Fang, Jin, M, Gong, C, Pan, S, Li, YF, Shirui Pan, Jin, Ming, Zhou, Chuan, Yuan-Fang Li, Yizhen Zheng, Pan, Shirui, Chuan Zhou, Ming Jin, Zheng, Y   +17 more
core   +1 more source

Emerging experimental and computational methods for studying redox‐regulated structural transitions

FEBS Letters, EarlyView.
Redox reactions can reshape proteins and alter how they behave in cells, with important consequences for health and disease. This review explores emerging experimental and computational approaches for discovering these redox‐sensitive protein switches, revealing their structural effects, and predicting their behavior, opening new opportunities to ...
Tasneem Rass, Dana Reichmann, Gábor Erdős   +2 more
wiley   +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