Graph Neural Network Features
Graph Neural Network Features is a computational construct that represents node, edge, or graph-level attributes derived through message-passing mechanisms in graph neural networks to encode structural and feature information for downstream learning tasks.
506
Publications
36.2K
Citations
2.2K
Authors
626
Institutions
Publications per year
2017–2026
504
Parents
506
Heterogeneous Graph Attention Network
Xiao Wang, Houye Ji, Chuan Shi et al. · 2019 · 2.9K citations · Full text
Skeleton-Based Action Recognition With Directed Graph Neural Networks
Lei Shi, Yifan Zhang, Jian Cheng et al. · 2019 · 934 citations
GNNExplainer: Generating Explanations for Graph Neural Networks
Rex Ying, Dylan Bourgeois, Jiaxuan You et al. · arXiv (Cornell University) · 2019 · 698 citations · Full text
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Chinese Academy of Sciences
Beijing, China
17 h-index
55 publications
8.5K citations

Shanghai Jiao Tong University
Shanghai, China
16 h-index
53 publications
4K citations
University of Chinese Academy of Sciences
Beijing, China
15 h-index
49 publications
7.5K citations
Tsinghua University
Beijing, China
12 h-index
33 publications
4.7K citations
Carnegie Mellon University
Pittsburgh, United States
12 h-index
33 publications
3.6K citations

Peking University
Beijing, China
12 h-index
28 publications
779 citations
Nanyang Technological University
Singapore, Singapore
12 h-index
31 publications
3.6K citations

Beijing University of Posts and Telecommunications
Beijing, China
11 h-index
42 publications
15K citations
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Repository · 71 publications · 4.1K citations
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12 publications · 536 citations
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Conference · 11 publications · 2.7K citations
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11 publications · 512 citations
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9 publications · 633 citations
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