  Recently, there has been an interest in embedding networks in hyperbolic
space, since hyperbolic space has been shown to work well in capturing
graph/network structure as it can naturally reflect some properties of complex
networks. However, the work on network embedding in hyperbolic space has been
focused on microscopic node embedding. In this work, we are the first to
present a framework to embed the structural roles of nodes into hyperbolic
space. Our framework extends struct2vec, a well-known structural role
preserving embedding method, by moving it to a hyperboloid model. We evaluated
our method on four real-world and one synthetic network. Our results show that
hyperbolic space is more effective than euclidean space in learning latent
representations for the structural role of nodes.
