Graph Feature Construction
Graph Feature Construction is a feature engineering methodology that specifically involves the systematic derivation of informative and discriminative attributes from graph-structured data by leveraging topological, structural, and semantic properties inherent to graphs, thereby enabling enhanced machine learning model performance on graph-related tasks.
116
Publications
11.2K
Citations
473
Authors
210
Institutions
Publications per year
2017–2026
75
116
A novel graph convolutional feature based convolutional neural network for stock trend prediction
Wei Chen, Manrui Jiang, Wei-Guo Zhang et al. · Information Sciences · 2020 · 276 citations
Computing, Data Science, Finance +16
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210

Xidian University
Xi'an, China
5 h-index
17 publications
678 citations
KU Leuven
Leuven, Belgium
4 h-index
12 publications
300 citations

Chinese Academy of Sciences
Beijing, China
4 h-index
12 publications
335 citations

Huazhong University of Science and Technology
Wuhan, China
4 h-index
11 publications
190 citations

Central South University
Changsha, China
4 h-index
12 publications
251 citations

Chongqing University
Chongqing, China
3 h-index
11 publications
550 citations
Purdue University West Lafayette
West Lafayette, United States
3 h-index
5 publications
657 citations
University of Science and Technology of China
Hefei, China
3 h-index
9 publications
315 citations
1–8 of 210
9 publications · 303 citations
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Repository · 6 publications · 815 citations
6
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3 publications · 117 citations
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TKDE · 3 publications · 303 citations
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3 publications · 54 citations
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1–5