Epitomic Variational Graph Autoencoder

Variational autoencoder (VAE) is a widely used generative model for unsupervised learning of vector data. The learning capacity of VAE is often limited by \textit{over-pruning} - a phenomenon that prevents many of the latent dimensions from learning any useful information about the input data. Variational graph autoencoder (VGAE) extends VAE for unsupervised learning of graph-structured data. Being an extension of VAE model, VGAE, also suffers from over-pruning in principal. In this paper we look at over-pruning in VGAE and observe that the generative capacity of VGAE is limited because of the way VGAE deals with this issue. We then propose epitomic variational graph autoencoder (EVGAE), a generative variational framework for graph datasets to overcome over-pruning. We show through experiments that the resulting model has a better generative ability and also achieves better scores in graph analysis related tasks.