struc2vec is a framework to generate node vector representations on a graph that preserve the structural identity.
[2] In particular, struc2vec employs a degree-based method to measure the pairwise structural role similarity, which is then adopted to build the multi-layer graph.
Moreover, the distance between the latent representation of nodes is strongly correlated to their structural similarity.
[3] Sequences of nodes are fed into a skip-gram or continuous bag of words model and traditional machine-learning techniques for classification can be used.
[4] It is considered a useful framework to learn node embeddings based on structural equivalence.