Skip to main content

3 Result(s)

within Seiji Maekawa Remove this filter

and

  1. No Access

    Chapter and Conference Paper

    Benchmarking GNNs with GenCAT Workbench

    We present GenCAT Workbench, an end-to-end framework with which users can generate synthetic attributed graphs with node labels and evaluate their graph analytic methods, e.g., graph neural networks (GNNs), on...

    Seiji Maekawa, Yuya Sasaki, George Fletcher… in Machine Learning and Knowledge Discovery i… (2023)

  2. No Access

    Chapter and Conference Paper

    GNN Transformation Framework for Improving Efficiency and Scalability

    We propose a framework that automatically transforms non-scalable GNNs into precomputation-based GNNs which are efficient and scalable for large-scale graphs. The advantages of our framework are two-fold; 1) i...

    Seiji Maekawa, Yuya Sasaki, George Fletcher… in Machine Learning and Knowledge Discovery i… (2023)

  3. No Access

    Chapter and Conference Paper

    Adaptive Node Embedding Propagation for Semi-supervised Classification

    Graph Convolutional Networks (GCNs) are state-of-the-art approaches for semi-supervised node classification task. By increasing the number of layers, GCNs utilize high-order relations between nodes that are mo...

    Yuya Ogawa, Seiji Maekawa, Yuya Sasaki… in Machine Learning and Knowledge Discovery i… (2021)