  We examine two fundamental tasks associated with graph representation
learning: link prediction and semi-supervised node classification. We present a
novel autoencoder architecture capable of learning a joint representation of
both local graph structure and available node features for the multi-task
learning of link prediction and node classification. Our autoencoder
architecture is efficiently trained end-to-end in a single learning stage to
simultaneously perform link prediction and node classification, whereas
previous related methods require multiple training steps that are difficult to
optimize. We provide a comprehensive empirical evaluation of our models on nine
benchmark graph-structured datasets and demonstrate significant improvement
over related methods for graph representation learning. Reference code and data
are available at https://github.com/vuptran/graph-representation-learning
