Graph Contrastive Learning with Local and Global Mutual Information Maximization
Abstract
References
Recommendations
GCH: Graph Contrastive Learning with Higher-Order Networks
Web and Big DataAbstractGraph contrastive learning has improved graph representation learning, becoming a successful unsupervised graph representation learning method. This method first generates two or more views of the input graph through data augmentation and learns ...
Self-supervised Graph Learning with Segmented Graph Channels
Machine Learning and Knowledge Discovery in DatabasesAbstractSelf-supervised graph learning adopts self-defined signals as supervision to learn representations. This learning paradigm solves the critical problem of utilizing unlabeled graph data. Conventional self-supervised graph learning methods rely on ...
Towards Unsupervised Deep Graph Structure Learning
WWW '22: Proceedings of the ACM Web Conference 2022In recent years, graph neural networks (GNNs) have emerged as a successful tool in a variety of graph-related applications. However, the performance of GNNs can be deteriorated when noisy connections occur in the original graph structures; besides, the ...
Comments
Information & Contributors
Information
Published In

Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 208Total Downloads
- Downloads (Last 12 months)20
- Downloads (Last 6 weeks)1
Other Metrics
Citations
Cited By
View allView Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format