  We introduce the variational graph auto-encoder (VGAE), a framework for
unsupervised learning on graph-structured data based on the variational
auto-encoder (VAE). This model makes use of latent variables and is capable of
learning interpretable latent representations for undirected graphs. We
demonstrate this model using a graph convolutional network (GCN) encoder and a
simple inner product decoder. Our model achieves competitive results on a link
prediction task in citation networks. In contrast to most existing models for
unsupervised learning on graph-structured data and link prediction, our model
can naturally incorporate node features, which significantly improves
predictive performance on a number of benchmark datasets.

Machine Learning;Machine Learning