  Learning embeddings from large-scale networks is an open challenge. Despite
the overwhelming number of existing methods, is is unclear how to exploit
network structure in a way that generalizes easily to unseen nodes, edges or
graphs. In this work, we look at the problem of finding inductive network
embeddings in large networks without domain-dependent node/edge attributes. We
propose to use a set of basic predefined local encodings as the basis of a
learning algorithm. In particular, we consider the degree frequencies at
different distances from a node, which can be computed efficiently for
relatively short distances and a large number of nodes. Interestingly, the
resulting embeddings generalize well across unseen or distant regions in the
network, both in unsupervised settings, when combined with language model
learning, as well as in supervised tasks, when used as additional features in a
neural network. Despite its simplicity, this method achieves state-of-the-art
performance in tasks such as role detection, link prediction and node
classification, and represents an inductive network embedding method directly
applicable to large unattributed networks.

Machine Learning;Machine Learning