  Network embedding aims to represent each node in a network as a
low-dimensional feature vector that summarizes the given node's (extended)
network neighborhood. The nodes' feature vectors can then be used in various
downstream machine learning tasks. Recently, many embedding methods that
automatically learn the features of nodes have emerged, such as node2vec and
struc2vec, which have been used in tasks such as node classification, link
prediction, and node clustering, mainly in the social network domain. There are
also other embedding methods that explicitly look at the connections between
nodes, i.e., the nodes' network neighborhoods, such as graphlets. Graphlets
have been used in many tasks such as network comparison, link prediction, and
network clustering, mainly in the computational biology domain. Even though the
two types of embedding methods (node2vec/struct2vec versus graphlets) have a
similar goal -- to represent nodes as features vectors, no comparisons have
been made between them, possibly because they have originated in the different
domains. Therefore, in this study, we compare graphlets to node2vec and
struc2vec, and we do so in the task of network alignment. In evaluations on
synthetic and real-world biological networks, we find that graphlets are both
more accurate and faster than node2vec and struc2vec.
