Neighborhood Aggregation

Neighborhood Aggregation is a computational mechanism in graph-based machine learning that, as a form of message-passing operation, selectively pools feature information from a node's adjacent neighbors according to a specified aggregation function to update the node's representation in a topology-aware manner.

123

Publications

12.3K

Citations

454

Authors

165

Institutions

Publications per year

2017–2026

105

Authors

454

  1. XH
    Xiangnan He

    University of Science and Technology of China, China

    5 publications · 580 citations

    5

    h-index

  2. JH
    Jiawei Han

    University of Illinois Urbana-Champaign, United States

    3 publications · 137 citations

    3

    h-index

  3. FF
    Fuli Feng

    University of Science and Technology of China, China

    3 publications · 123 citations

    3

    h-index

  4. SV
    Shikhar Vashishth

    Indian Institute of Science Bangalore, India

    3 publications · 45 citations

    3

    h-index

  5. KX
    Keyulu Xu

    Massachusetts Institute of Technology, United States

    3 publications · 1.5K citations

    3

    h-index

1–5 of 454

Venues

  1. arXiv (Cornell University)

    Repository · 26 publications · 2.2K citations

    19

    h-index

  2. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 6 publications · 151 citations

    6

    h-index

  3. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 5 publications · 317 citations

    5

    h-index

  4. IEEE Transactions on Multimedia

    3 publications · 124 citations

    3

    h-index

  5. IEEE Access

    3 publications · 78 citations

    3

    h-index

1–5