Edge Embeddings
Edge Embeddings is a representation learning technique that captures the structural and semantic characteristics of edges in a graph by mapping them into low-dimensional vector spaces, distinguishing itself from node or graph-level embeddings by explicitly modeling pairwise relationships between vertices.
187
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7.5K
Citations
661
Authors
277
Institutions
Publications per year
2017–2026
107
Parents
187
A Comprehensive Survey on Deep Graph Representation Learning
Wei Ju, Zheng Fang, Yiyang Gu et al. · Neural Networks · 2024 · 305 citations
PyTorch-BigGraph: A Large-scale Graph Embedding System
Adam Lerer, Ledell Wu, Jiajun Shen et al. · arXiv (Cornell University) · 2019 · 205 citations · Full text
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Tsinghua University
Beijing, China
5 h-index
15 publications
826 citations
Adobe Systems (United States)
San Jose, United States
4 h-index
9 publications
368 citations

Sapienza University of Rome
Rome, Italy
4 h-index
9 publications
161 citations

University of York
York, United Kingdom
4 h-index
6 publications
224 citations
University of Arizona
Tucson, United States
4 h-index
8 publications
179 citations

Beihang University
Beijing, China
4 h-index
6 publications
150 citations
University of Michigan
Ann Arbor, United States
4 h-index
9 publications
353 citations

University of Ljubljana
Ljubljana, Slovenia
3 h-index
3 publications
84 citations
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Repository · 16 publications · 658 citations
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11 publications · 313 citations
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9 publications · 417 citations
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9 publications · 457 citations
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6 publications · 160 citations
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