Heterogeneous Graph Features
Heterogeneous Graph Features is a graph representation construct that encapsulates node and edge attributes in graphs with multiple types of entities and relations, enabling the differentiation of structural and semantic heterogeneity inherent in complex relational data.
114
Publications
10.7K
Citations
473
Authors
177
Institutions
Publications per year
2017–2026
98
Parents
114
Heterogeneous Graph Attention Network
Xiao Wang, Houye Ji, Chuan Shi et al. · 2019 · 2.9K citations · Full text
Heterogeneous Graph Neural Network
Chuxu Zhang, Dongjin Song, Chao Huang et al. · 2019 · 1.5K citations · Full text
Heterogeneous Graph Attention Networks for Semi-supervised Short Text Classification
Linmei Hu, Tianchi Yang, Chuan Shi et al. · 2019 · 339 citations · Full text
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177

Chinese Academy of Sciences
Beijing, China
9 h-index
20 publications
2.5K citations
Tsinghua University
Beijing, China
6 h-index
15 publications
4.7K citations
University of Chinese Academy of Sciences
Beijing, China
5 h-index
14 publications
1.1K citations

Alibaba Group (China)
Hangzhou, China
5 h-index
6 publications
362 citations

Beihang University
Beijing, China
5 h-index
14 publications
496 citations
Microsoft Research Asia (China)
Beijing, China
4 h-index
7 publications
326 citations

Beijing University of Posts and Telecommunications
Beijing, China
4 h-index
15 publications
13.7K citations
Institute of Information Engineering
Beijing, China
4 h-index
11 publications
1.3K citations
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Repository · 8 publications · 179 citations
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5 publications · 379 citations
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Conference · 5 publications · 607 citations
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4 publications · 333 citations
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3 publications · 305 citations
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