KGNN: Knowledge Graph Neural Network for Drug-Drug Interaction Prediction. IJCAI' 20 accepted. Figure 1 shows the overview of KGNN. It takes the parsed DDI matrix and …

To address the above limitations, we propose an end-to-end framework, called Knowledge Graph Neural Network (KGN-N), to resolve the DDI prediction. Our frame-work can effectively …

To address the above limitations, we propose an end-to-end framework, called Knowledge Graph Neural Network (KGNN), to resolve the DDI prediction. Our framework can effectively capture …

为解决上述局限性，林轩等人提出了一种端到端的框架，即基于知识图谱的图神经网络（KGNN），以解决DDI预测问题。 该框架可通过在KG中挖掘相关联的关系，来有效地捕获 …

KGNN-LS applies the technique of graph neural networks (GNNs) to proces knowledge graphs for the purpose of recommendation. The model is enhanced by adding a label smoothness …

May 17, 2022 · To address these issues, we develop a novel framework called KGNN to take full advantage of knowledge data for representation learning in the distributed learning system.

May 21, 2022 · We address the limitations by proposing the Kernel-based Graph Neural Network (KGNN). A KGNN consists of a GNN-based network as well as a kernel-based network …

May 11, 2019 · Here we propose Knowledge-aware Graph Neural Networks with Label Smoothness regularization (KGNN-LS) to provide better recommendations. Conceptually, our …

To address the above limitations, we propose an end-to-end framework, called Knowledge Graph Neural Network (KGNN), to resolve the DDI prediction. Our framework can effectively capture …

Using a self-constructed dataset, experiments compared the performance of KGNN with traditional GNN-based methods. The results demonstrate that KGNN significantly outperforms …