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

Authors

449

  1. AH
    Ali H. Sayed

    University of California, Los Angeles, United States

    5 publications · 138 citations

    5

    h-index

  2. SV
    Stefan Vlaski

    École Polytechnique Fédérale de Lausanne, Switzerland

    4 publications · 93 citations

    4

    h-index

  3. DZ
    Da Zheng

    Amazon (United States), United States

    4 publications · 296 citations

    4

    h-index

  4. HY
    Hongxia Yang

    Alibaba Group (China), China

    4 publications · 480 citations

    4

    h-index

  5. RN
    Roula Nassif

    Université Côte d'Azur, France

    4 publications · 117 citations

    4

    h-index

1–5 of 449

Venues

  1. arXiv (Cornell University)

    Repository · 24 publications · 773 citations

    15

    h-index

  2. Proceedings of the VLDB Endowment

    PVLDB · 4 publications · 440 citations

    4

    h-index

  3. IEEE Transactions on Signal Processing

    3 publications · 163 citations

    3

    h-index

  4. IEEE Transactions on Automatic Control

    3 publications · 96 citations

    3

    h-index

  5. 3

    h-index

1–5