Self-attention graph pooling

Junhyun Lee, Inyeop Lee, Jaewoo Kang

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    37 Citations (Scopus)


    Advanced methods of applying deep learning to structured data such as graphs have been proposed in recent years. In particular, studies have focused on generalizing convolutional neural networks to graph data, which includes redefining the convolution and the downsampling (pooling) operations for graphs. The method of generalizing the convolution operation to graphs has been proven to improve performance and is widely used. However, the method of applying down-sampling to graphs is still difficult to perform and has room for improvement. In this paper, we propose a graph pooling method based on self-attention. Self-attention using graph convolution allows our pooling method to consider both node features and graph topology. To ensure a fair comparison, the same training procedures and model architectures were used for the existing pooling methods and our method. The experimental results demonstrate that our method achieves superior graph classification performance on the benchmark datasets using a reasonable number of parameters.

    Original languageEnglish
    Title of host publication36th International Conference on Machine Learning, ICML 2019
    PublisherInternational Machine Learning Society (IMLS)
    Number of pages10
    ISBN (Electronic)9781510886988
    Publication statusPublished - 2019
    Event36th International Conference on Machine Learning, ICML 2019 - Long Beach, United States
    Duration: 2019 Jun 92019 Jun 15

    Publication series

    Name36th International Conference on Machine Learning, ICML 2019


    Conference36th International Conference on Machine Learning, ICML 2019
    Country/TerritoryUnited States
    CityLong Beach

    ASJC Scopus subject areas

    • Education
    • Computer Science Applications
    • Human-Computer Interaction


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