Spectral Graph Learning
Spectral Graph Learning is a graph-based learning framework that leverages the spectral decomposition of graph operators, such as the graph Laplacian, to extract and exploit low-dimensional representations of graph structure by transforming graph data into a spectral domain where topological patterns are more discernible.
216
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48K
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703
Authors
291
Institutions
Publications per year
2017–2026
132
Parents
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Guillem Cucurull, Arantxa Casanova, Adriana Romero et al. · arXiv (Cornell University) · 2017 · 8.3K citations · Full text
Zhiyong Cui, Kristian Henrickson, Ruimin Ke et al. · IEEE Transactions on Intelligent Transportation Systems · 2019 · 955 citations
Petar Veličković, Guillem Cucurull, Arantxa Casanova et al. · arXiv (Cornell University) · 2017 · 947 citations · Full text
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Northwestern Polytechnical University
Xi'an, China
7 h-index
25 publications
2.1K citations

Shanghai Jiao Tong University
Shanghai, China
5 h-index
15 publications
431 citations

Indian Institute of Science Bangalore
Bengaluru, India
5 h-index
12 publications
323 citations
Tsinghua University
Beijing, China
5 h-index
12 publications
493 citations

Peking University
Beijing, China
5 h-index
13 publications
66.1K citations
Yahoo (United States)
Sunnyvale, United States
4 h-index
5 publications
423 citations

Chinese Academy of Sciences
Beijing, China
4 h-index
9 publications
1K citations
Nanjing University of Science and Technology
Nanjing, China
4 h-index
16 publications
512 citations
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Repository · 36 publications · 19.2K citations
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6 publications · 310 citations
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5 publications · 449 citations
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4 publications · 103 citations
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4 publications · 113 citations
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