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 knowledge graph obtained from preprocessing of dataset as the input. It outputs the interaction value for the drug-drug pair. To run the code, you need the following dependencies:

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 capture drug and its potential neighborhoods by mining their associated relation-s in KG.

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 drug and its potential neighborhoods by mining their associated relations in KG.

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，来有效地捕获药物及其潜在的邻域实体信息。 为了提取KG中的高阶结构和语义关系，对KG中每个实体的邻域进行学习，作为它们的局部感知域，然后将邻域信息与来自当前实体表示的偏差进行整合。 这样，感知域可以自然地扩展到多个跃点，以对高阶拓扑信息进行建模并获得潜在的药物长距离相关性特征。 …

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 regularizer for more powerful and adaptive learning. src/: implementations of KGNN-LS. (The raw rating file of MovieLens-20M is too large to be contained in this repository.

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 parameterized by a memory network.

May 11, 2019 · Here we propose Knowledge-aware Graph Neural Networks with Label Smoothness regularization (KGNN-LS) to provide better recommendations. Conceptually, our approach computes user-specific item embeddings by first applying a trainable function that identifies important knowledge graph relationships for a given user.

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 traditional models in terms of accuracy, recall, and F1-score, showcasing its effectiveness and superiority in APT detection tasks.