Node Embeddings is a representational technique in graph feature engineering that maps vertices in a graph to low-dimensional vector spaces such that the geometric relationships in the embedding space preserve the structural and semantic properties of the nodes within the graph.
869
Publications
63.4K
Citations
2.6K
Authors
690
Institutions
Publications per year
2017–2026
797
Variants
Node Embedding
Parents
Publications
869
Inductive Representation Learning on Large Graphs
William L. Hamilton, Rex Ying, Jure Leskovec · arXiv (Cornell University) · 2017 · 4.5K citations · Full text
Yuxiao Dong, Nitesh V. Chawla, Ananthram Swami · 2017 · 2.2K citations
1–5 of 869
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2.6K
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Institutions
690
Tsinghua University
Beijing, China
22 h-index
79 publications
12.9K citations
Beijing University of Posts and Telecommunications
Beijing, China
19 h-index
60 publications
7.8K citations
University of Technology Sydney
Sydney, Australia
19 h-index
48 publications
8.2K citations
University of Illinois Urbana-Champaign
Urbana, United States
19 h-index
52 publications
5K citations
Beihang University
Beijing, China
18 h-index
58 publications
4.1K citations
Tencent (China)
Shenzhen, China
17 h-index
46 publications
3.8K citations
University of Illinois Chicago
Chicago, United States
17 h-index
30 publications
3.1K citations
Chinese Academy of Sciences
Beijing, China
16 h-index
54 publications
2.7K citations
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Venues
- arXiv (Cornell University)
Repository · 110 publications · 10.5K citations
40
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- Proceedings of the AAAI Conference on Artificial Intelligence
Conference · 36 publications · 2.9K citations
25
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- IEEE Transactions on Knowledge and Data Engineering
TKDE · 41 publications · 4.4K citations
23
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- Pattern Recognition
20 publications · 1.1K citations
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- IEEE Transactions on Neural Networks and Learning Systems
17 publications · 872 citations
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