Transcription of Self-supervised Heterogeneous Graph Neural Network with …
{{id}} {{{paragraph}}}
Example: marketing
Self-supervised Heterogeneous Graph Neural Network with …
Self-supervised Heterogeneous Graph Neural Network withCo-contrastive LearningXiao University of Posts andTelecommunicationsBeijing, ChinaNian University of Posts andTelecommunicationsBeijing, ChinaHui University of Posts andTelecommunicationsBeijing, ChinaChuan Shi University of Posts andTelecommunicationsBeijing, ChinaABSTRACTH eterogeneous Graph Neural networks (HGNNs) as an emergingtechnique have shown superior capacity of dealing with heteroge-neous information Network (HIN). However, most HGNNs follow asemi-supervised learning manner, which notably limits their wideuse in reality since labels are usually scarce in real applications. Re-cently, contrastive learning, a Self-supervised method, becomes oneof the most exciting learning paradigms and shows great potentialwhen there are no labels.
Telecommunications Beijing, China Hui Han hanhui@bupt.edu.cn Beijing University of Posts and Telecommunications Beijing, China Chuan Shi∗ shichuan@bupt.edu.cn Beijing University of Posts and Telecommunications Beijing, China ABSTRACT Heterogeneous graph neural networks (HGNNs) as an emerging technique have shown superior capacity of dealing ...
Loading..
Tags:
Information
Domain:
Source:
Link to this page:
Please notify us if you found a problem with this document: