In this article, we present a knowledge graph embedding based graph convolutional network for link prediction ... The experimental results demonstrate that our model achieves improved performance ...

ConvE uses embedded two-dimensional convolution and multi-layer nonlinear features to model the knowledge ... graph structure data, ConvE does not make effective use of this. The recent Graph ...

GCN is a multilayer connected neural network architecture and is ... integration module and the knowledge graph embedding module, and feed the fused features into the graph convolutional network ...

The neural network model developed for this task ... To address this gap, this study proposes a novel end-to-end model called the Dual Graph Convolutional Networks Integrating Affective Knowledge and ...

Therefore,we proposed a computational method for predicting circRNA-miRNA interacrtions based on the knowledge graph enhanced ... the embedding was fed into classifier for prediction. An independent ...