Graph Representation Learning is a computational framework within representation learning that transforms graph-structured data into low-dimensional vector spaces while preserving topological and semantic properties unique to graphs.
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2017–2026
3.2K
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3.7K
Aditya Grover, Jure Leskovec · 2016 · 10.9K citations
Guillem Cucurull, Arantxa Casanova, Adriana Romero et al. · arXiv (Cornell University) · 2017 · 8.3K citations · Full text
Deep Learning On Graphs (Graphsip Summer School)
Michaël Defferrard · Figshare · 2016 · 5.1K citations
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Tsinghua University
Beijing, China
59 h-index
359 publications
49.7K citations
Chinese Academy of Sciences
Beijing, China
48 h-index
366 publications
33K citations
Peking University
Beijing, China
44 h-index
286 publications
23.5K citations
University of Technology Sydney
Sydney, Australia
41 h-index
166 publications
31.5K citations
University of Electronic Science and Technology of China
Chengdu, China
38 h-index
182 publications
14.2K citations
Beijing University of Posts and Telecommunications
Beijing, China
37 h-index
171 publications
20.6K citations
Northwestern Polytechnical University
Xi'an, China
36 h-index
158 publications
10.4K citations
University of Chinese Academy of Sciences
Beijing, China
36 h-index
210 publications
25K citations
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Repository · 715 publications · 65.3K citations
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- Proceedings of the AAAI Conference on Artificial Intelligence
Conference · 166 publications · 19.1K citations
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- IEEE Transactions on Knowledge and Data Engineering
TKDE · 100 publications · 8K citations
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- IEEE Transactions on Neural Networks and Learning Systems
103 publications · 5.5K citations
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- Pattern Recognition
95 publications · 5.1K citations
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