Graph Neural Networks: Taxonomy, Advances and Trends release_xj2kambrabfj3g5ldenfyixzu4

by Yu Zhou, Haixia Zheng, Xin Huang, Shufeng Hao, Dengao Li, Jumin Zhao

Released as a article .

2022  

Abstract

Graph neural networks provide a powerful toolkit for embedding real-world graphs into low-dimensional spaces according to specific tasks. Up to now, there have been several surveys on this topic. However, they usually lay emphasis on different angles so that the readers can not see a panorama of the graph neural networks. This survey aims to overcome this limitation, and provide a comprehensive review on the graph neural networks. First of all, we provide a novel taxonomy for the graph neural networks, and then refer to up to 400 relevant literatures to show the panorama of the graph neural networks. All of them are classified into the corresponding categories. In order to drive the graph neural networks into a new stage, we summarize four future research directions so as to overcome the facing challenges. It is expected that more and more scholars can understand and exploit the graph neural networks, and use them in their research community.
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Type  article
Stage   submitted
Date   2022-01-21
Version   v3
Language   en ?
arXiv  2012.08752v3
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Revision: 35344361-5f77-4ba6-be35-fab07052e13a
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