Global Attention-Based Graph Neural Networks for Node Classification

Jiusheng Chen; Chengyuan Fang; Xiaoyu Zhang

doi:10.1007/s11063-022-11032-z

ScienceGate Book Chapters

JOURNAL ARTICLE

Global Attention-Based Graph Neural Networks for Node Classification

Jiusheng Chen Chengyuan Fang Xiaoyu Zhang

Year: 2022 Journal: Neural Processing Letters Vol: 55 (4)Pages: 4127-4150 Publisher: Springer Science+Business Media

DOI: 10.1007/s11063-022-11032-z

Get Full-Text PDF Get Analytical Report

Keywords:

Computer science Graph Clustering coefficient Node (physics) Computational intelligence Artificial neural network Artificial intelligence Data mining Cluster analysis Theoretical computer science Machine learning

Metrics

Cited By

0.78

FWCI (Field Weighted Citation Impact)

Refs

0.71

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Advanced Graph Neural Networks

Physical Sciences → Computer Science → Artificial Intelligence

Traffic Prediction and Management Techniques

Physical Sciences → Engineering → Building and Construction

Machine Learning and ELM

Physical Sciences → Computer Science → Artificial Intelligence

Global Attention-Based Graph Neural Networks for Node Classification

Metrics

Citation History

Topics

Related Documents

Global-local graph neural networks for node-classification

Revisiting Attention-Based Graph Neural Networks for Graph Classification

Graph Neural Networks for Node Classification

Distributed Node Classification with Graph Attention Networks

Heterogeneous Network Node Classification Based on Graph Neural Networks