  Graph autoencoders (AE) and variational autoencoders (VAE) recently emerged
as powerful node embedding methods, with promising performances on challenging
tasks such as link prediction and node clustering. Graph AE, VAE and most of
their extensions rely on graph convolutional networks (GCN) to learn vector
space representations of nodes. In this paper, we propose to replace the GCN
encoder by a simple linear model w.r.t. the adjacency matrix of the graph. For
the two aforementioned tasks, we empirically show that this approach
consistently reaches competitive performances w.r.t. GCN-based models for
numerous real-world graphs, including the widely used Cora, Citeseer and Pubmed
citation networks that became the de facto benchmark datasets for evaluating
graph AE and VAE. This result questions the relevance of repeatedly using these
three datasets to compare complex graph AE and VAE models. It also emphasizes
the effectiveness of simple node encoding schemes for many real-world
applications.
