  Random walks are at the heart of many existing network embedding methods.
However, such algorithms have many limitations that arise from the use of
random walks, e.g., the features resulting from these methods are unable to
transfer to new nodes and graphs as they are tied to vertex identity. In this
work, we introduce the Role2Vec framework which uses the flexible notion of
attributed random walks, and serves as a basis for generalizing existing
methods such as DeepWalk, node2vec, and many others that leverage random walks.
Our proposed framework enables these methods to be more widely applicable for
both transductive and inductive learning as well as for use on graphs with
attributes (if available). This is achieved by learning functions that
generalize to new nodes and graphs. We show that our proposed framework is
effective with an average AUC improvement of 16.55% while requiring on average
853x less space than existing methods on a variety of graphs.
