Graph Neural Networks
Graph Neural Network is a type of neural network model that belongs to the broader class of deep learning architectures and is specifically designed to process and learn representations from graph-structured data by propagating and aggregating information across nodes and edges.
7.4K
Publications
636.2K
Citations
24.7K
Authors
3K
Institutions
Publications per year
2017–2026
7.3K
7.4K
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio et al. · Proceedings of the IEEE · 1998 · 58.2K citations · Full text
The Graph Neural Network Model
Franco Scarselli, M. Gori, Ah Chung Tsoi et al. · IEEE Transactions on Neural Networks · 2008 · 9.3K citations · Full text
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Tsinghua University
Beijing, China
80 h-index
741 publications
84.9K citations

Chinese Academy of Sciences
Beijing, China
72 h-index
773 publications
79.9K citations

Peking University
Beijing, China
63 h-index
530 publications
105K citations
University of Chinese Academy of Sciences
Beijing, China
59 h-index
552 publications
52.9K citations

Shanghai Jiao Tong University
Shanghai, China
54 h-index
476 publications
32K citations

Beihang University
Beijing, China
51 h-index
399 publications
25.6K citations
University of Science and Technology of China
Hefei, China
51 h-index
327 publications
36.2K citations

Alibaba Group (China)
Hangzhou, China
51 h-index
250 publications
17.2K citations
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Repository · 1.2K publications · 90.8K citations
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Conference · 253 publications · 34.2K citations
79
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127 publications · 7.8K citations
48
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TKDE · 114 publications · 10.5K citations
47
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TPAMI · 73 publications · 7.1K citations
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