Recent literature has shown several benefits of hyperbolic embedding of graph-structured data (GSD) in representing their structures and latent relations. While several studies have explored the ability of hyperbolic embedding to represent data (for example, by quantifying their mean average precision) and their ability to produce better visualisations of clusters, only few works exploited the effectiveness of hyperbolic embedding to perform learning on the initial GSD. Motivated by innovative ideas from the fields of Brain computer interfaces and Radar processing, this paper presents a new scheme for learning GSD based on hyperbolic embedding, Riemannian barycentre (i.e. Fr\'echet or geometric mean) and $K$-means algorithms as a significant tool that derives from it. The main idea is as follows. Relying on the Riemannian barycentre, we define a notion of minimal variance which allows us to choose an embedding between different ones. This embedding is used thereafter together with $K$-means algorithms to perform unsupervised clustering and in combination with the nearest neighbour rule to perform supervised learning. We demonstrate the performance of the proposed framework through several experiments on real-world social networks and hierarchical GSD. The obtained results outperform their counterparts in high-dimensional Euclidean spaces and recent proposed geometric approaches.