  In this work, we have proposed an approach for improving the GCN for
predicting ratings in social networks. Our model is expanded from the standard
model with several layers of transformer architecture. The main focus of the
paper is on the encoder architecture for node embedding in the network. Using
the embedding layer from the graph-based convolution layer, the attention
mechanism could rearrange the feature space to get a more efficient embedding
for the downstream task. The experiments showed that our proposed architecture
achieves better performance than GCN on the traditional link prediction task.
