Distributed Graph Learning
Distributed Graph Learning is a computational framework that enables the collaborative training of graph-based machine learning models across multiple decentralized devices or nodes, where the learning process explicitly accounts for the underlying network topology and data distribution.
120
Publications
5.8K
Citations
449
Authors
172
Institutions
Publications per year
2017–2026
99
Parents
120
Rong Zhu, Kun Zhao, Hongxia Yang et al. · Proceedings of the VLDB Endowment · 2019 · 293 citations
Graph-based deep learning for communication networks: A survey
Weiwei Jiang · Computer Communications · 2021 · 273 citations · Full text
PyTorch-BigGraph: A Large-scale Graph Embedding System
Adam Lerer, Ledell Wu, Jiajun Shen et al. · arXiv (Cornell University) · 2019 · 205 citations · Full text
DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphs
Da Zheng, Chao Ma, Minjie Wang et al. · 2020 · 191 citations
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172
École Polytechnique Fédérale de Lausanne
Lausanne, Switzerland
6 h-index
17 publications
408 citations
Tsinghua University
Beijing, China
5 h-index
7 publications
579 citations
Aalborg University
Aalborg, Denmark
4 h-index
4 publications
91 citations
Alibaba Group (United States)
San Mateo, United States
3 h-index
30 publications
2.9K citations

Peking University
Beijing, China
3 h-index
3 publications
172 citations

Beijing University of Posts and Telecommunications
Beijing, China
3 h-index
8 publications
221 citations
Wuhan University
Wuhan, China
3 h-index
9 publications
340 citations

Sapienza University of Rome
Rome, Italy
3 h-index
4 publications
337 citations
1–8 of 172
Repository · 24 publications · 773 citations
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PVLDB · 4 publications · 440 citations
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3 publications · 163 citations
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3 publications · 96 citations
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3 publications · 74 citations
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