  We examine two fundamental tasks associated with graph representation
learning: link prediction and node classification. We present a new autoencoder
architecture capable of learning a joint representation of local graph
structure and available node features for the simultaneous multi-task learning
of unsupervised link prediction and semi-supervised node classification. Our
simple, yet effective and versatile model is efficiently trained end-to-end in
a single stage, whereas previous related deep graph embedding methods require
multiple training steps that are difficult to optimize. We provide an empirical
evaluation of our model on five benchmark relational, graph-structured datasets
and demonstrate significant improvement over three strong baselines for graph
representation learning. Reference code and data are available at
https://github.com/vuptran/graph-representation-learning
