Node Feature Engineering
Node Feature Engineering is a methodological framework within graph feature engineering that specifically involves the systematic design, transformation, and augmentation of attribute data associated with individual nodes to enhance the representational quality and predictive performance of graph-based machine learning models.
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12K
Citations
112
Authors
56
Institutions
Publications per year
2017–2026
21
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Aditya Grover, Jure Leskovec · 2016 · 10.9K citations
FLAG: Adversarial Data Augmentation for Graph Neural Networks
Kezhi Kong, Guohao Li, Mucong Ding et al. · arXiv (Cornell University) · 2021 · 73 citations · Full text
Enhanced Graph Isomorphism Network for Molecular ADMET Properties Prediction
Yuzhong Peng, Yanmei Lin, Xiao‐Yuan Jing et al. · IEEE Access · 2020 · 67 citations · Full text
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Stanford University
Stanford, United States
4 h-index
10 publications
22.4K citations

Northwestern Polytechnical University
Xi'an, China
2 h-index
7 publications
233 citations
Tongji University
Shanghai, China
2 h-index
3 publications
126 citations

United States Naval Academy
Annapolis, United States
2 h-index
2 publications
101 citations

Peking University
Beijing, China
1 h-index
3 publications
51 citations
Huaiyin Institute of Technology
Huai'an, China
1 h-index
4 publications
112 citations
San Jose State University
San Jose, United States
1 h-index
1 publications
24 citations
China University of Mining and Technology
Xuzhou, China
1 h-index
4 publications
172 citations
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4 publications · 179 citations
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Repository · 3 publications · 128 citations
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Conference · 2 publications · 152 citations
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2 publications · 41 citations
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1 publications · 20 citations
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