  We consider the question of embedding nodes with similar local neighborhoods
together in embedding space, commonly referred to as "role embeddings." We
propose RAE, an unsupervised framework that learns role embeddings. It combines
a within-node loss function and a graph neural network (GNN) architecture to
place nodes with similar local neighborhoods close in embedding space. We also
propose a faster way of generating negative examples called neighbor shuffling,
which quickly creates negative examples directly within batches. These
techniques can be easily combined with existing GNN methods to create
unsupervised role embeddings at scale. We then explore role action embeddings,
which summarize the non-structural features in a node's neighborhood, leading
to better performance on node classification tasks. We find that the model
architecture proposed here provides strong performance on both graph and node
classification tasks, in some cases competitive with semi-supervised methods.
