  We propose the Graph Context Encoder (GCE), a simple but efficient approach
for graph representation learning based on graph feature masking and
reconstruction.
  GCE models are trained to efficiently reconstruct input graphs similarly to a
graph autoencoder where node and edge labels are masked. In particular, our
model is also allowed to change graph structures by masking and reconstructing
graphs augmented by random pseudo-edges.
  We show that GCE can be used for novel graph generation, with applications
for molecule generation. Used as a pretraining method, we also show that GCE
improves baseline performances in supervised classification tasks tested on
multiple standard benchmark graph datasets.
