  Graph autoencoders are efficient at embedding graph-based data sets. Most
graph autoencoder architectures have shallow depths which limits their ability
to capture meaningful relations between nodes separated by multi-hops. In this
paper, we propose Residual Variational Graph Autoencoder, ResVGAE, a deep
variational graph autoencoder model with multiple residual modules. We show
that our multiple residual modules, a convolutional layer with residual
connection, improve the average precision of the graph autoencoders.
Experimental results suggest that our proposed model with residual modules
outperforms the models without residual modules and achieves similar results
when compared with other state-of-the-art methods.
