Variational Graph Auto-Encoders

We introduce the variational graph autoencoder (VGAE), a framework for unsupervised learning on graph-structured data based on the variational auto-encoder (VAE) [2, 3]. This model makes use of latent variables and is capable of learning interpretable latent representations for undirected graphs (see Figure 1). We demonstrate this model using a graph convolutional network (GCN) [4] 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 [5, 6, 7, 8], our model can naturally incorporate node features, which significantly improves predictive performance on a number of benchmark datasets.