[HTML][HTML] Multi-level attention pooling for graph neural networks: Unifying graph representations with multiple localities
Graph neural networks (GNNs) have been widely used to learn vector representation of
graph-structured data and achieved better task performance than conventional methods …
graph-structured data and achieved better task performance than conventional methods …
[PDF][PDF] Multi-level attention pooling for graph neural networks: Unifying graph representations with multiple localities
TD Itoh, T Kubo, K Ikeda - Neural Networks, 2022 - researchgate.net
abstract Graph neural networks (GNNs) have been widely used to learn vector
representation of graphstructured data and achieved better task performance than …
representation of graphstructured data and achieved better task performance than …
Multi-Level Attention Pooling for Graph Neural Networks: Unifying Graph Representations with Multiple Localities
TD Itoh, T Kubo, K Ikeda - arXiv e-prints, 2021 - ui.adsabs.harvard.edu
Graph neural networks (GNNs) have been widely used to learn vector representation of
graph-structured data and achieved better task performance than conventional methods …
graph-structured data and achieved better task performance than conventional methods …
Multi-level attention pooling for graph neural networks: Unifying graph representations with multiple localities.
TD Itoh, T Kubo, K Ikeda - Neural Networks: the Official Journal of …, 2021 - europepmc.org
Graph neural networks (GNNs) have been widely used to learn vector representation of
graph-structured data and achieved better task performance than conventional methods …
graph-structured data and achieved better task performance than conventional methods …
Multi-level attention pooling for graph neural networks: Unifying graph representations with multiple localities
TD Itoh, T Kubo, K Ikeda - Neural networks: the official …, 2022 - pubmed.ncbi.nlm.nih.gov
Graph neural networks (GNNs) have been widely used to learn vector representation of
graph-structured data and achieved better task performance than conventional methods …
graph-structured data and achieved better task performance than conventional methods …
Multi-Level Attention Pooling for Graph Neural Networks: Unifying Graph Representations with Multiple Localities
TD Itoh, T Kubo, K Ikeda - arXiv preprint arXiv:2103.01488, 2021 - arxiv.org
Graph neural networks (GNNs) have been widely used to learn vector representation of
graph-structured data and achieved better task performance than conventional methods …
graph-structured data and achieved better task performance than conventional methods …
[引用][C] Multi-level attention pooling for graph neural networks: Unifying graph representations with multiple localities
TD Itoh, T Kubo, K Ikeda - Neural Networks, 2022 - cir.nii.ac.jp
Multi-level attention pooling for graph neural networks: Unifying graph representations with
multiple localities | CiNii Research CiNii 国立情報学研究所 学術情報ナビゲータ[サイニィ] 詳細 …
multiple localities | CiNii Research CiNii 国立情報学研究所 学術情報ナビゲータ[サイニィ] 詳細 …
Multi-level attention pooling for graph neural networks:: Unifying graph representations with multiple localities
TD Itoh, T Kubo, K Ikeda - 2022 - dl.acm.org
Graph neural networks (GNNs) have been widely used to learn vector representation of
graph-structured data and achieved better task performance than conventional methods …
graph-structured data and achieved better task performance than conventional methods …