Spatial Graph Learning

Spatial Graph Learning is a graph-based learning paradigm that models relational data by explicitly capturing local neighborhood structures and topological dependencies through message-passing mechanisms defined directly on the graph's spatial domain.

70

Publications

4.5K

Citations

259

Authors

118

Institutions

Publications per year

2017–2026

65

Authors

259

  1. HG
    Hanyu Guo

    Carnegie Mellon University, United States

    2 publications · 55 citations

    2

    h-index

  2. RU
    Raquel Urtasun

    University of Toronto, Canada

    2 publications · 224 citations

    2

    h-index

  3. JZ
    Jingbo Zhou

    Baidu (China), China

    2 publications · 68 citations

    2

    h-index

  4. SC
    Sergio Casas

    Universitat de València, Spain

    2 publications · 224 citations

    2

    h-index

  5. GJ
    Guangyin Jin

    National University of Defense Technology, China

    2 publications · 38 citations

    2

    h-index

1–5 of 259

Venues

  1. arXiv (Cornell University)

    Repository · 5 publications · 135 citations

    5

    h-index

  2. Neurocomputing

    3 publications · 217 citations

    3

    h-index

  3. 3

    h-index

  4. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 3 publications · 178 citations

    3

    h-index

  5. Process Safety and Environmental Protection

    2 publications · 82 citations

    2

    h-index

1–5