Low-dimensional embeddings of nodes in large graphs have proved extremely
useful in a variety of prediction tasks, from content recommendation to
identifying protein functions. However, most existing approaches require that
all nodes in the graph are present during training of the embeddings; these
previous approaches are inherently transductive and do not n