Graph Attention Networks

Graph Attention Networks is a neural network model that generalizes the attention mechanism to graph-structured data by enabling nodes to attend to their neighbors' features through learnable weighted aggregation, thereby distinguishing itself from other graph neural networks through its adaptive and context-dependent weighting of neighbor contributions.

1.4K

Publications

98.7K

Citations

5.6K

Authors

1K

Institutions

Publications per year

2017–2026

1.4K

Variants

Gats

Authors

5.6K

  1. CS
    Chuan Shi

    Beijing University of Posts and Telecommunications, China

    7 publications · 3.5K citations

    7

    h-index

  2. QZ
    Qi Zhao

    University of Science and Technology Liaoning, China

    7 publications · 826 citations

    7

    h-index

  3. GW
    Guangtao Wang

    Xi'an Jiaotong University, China

    7 publications · 242 citations

    7

    h-index

  4. ZY
    Zhu‐Hong You

    Xinjiang Technical Institute of Physics & Chemistry, China

    7 publications · 261 citations

    7

    h-index

  5. JL
    Jiawei Luo

    Hunan University, China

    6 publications · 423 citations

    6

    h-index

1–5 of 5.6K

Venues

  1. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 46 publications · 4.8K citations

    31

    h-index

  2. arXiv (Cornell University)

    Repository · 96 publications · 14K citations

    31

    h-index

  3. Briefings in Bioinformatics

    37 publications · 2.7K citations

    27

    h-index

  4. Expert Systems with Applications

    31 publications · 1.4K citations

    24

    h-index

  5. Knowledge-Based Systems

    35 publications · 1.3K citations

    23

    h-index

1–5