The embedding of complex networks into metric spaces has emerged as a prominent area of research, accompanied by a diverse array of proposed methodologies. Low-dimensional hyperbolic spaces provide a ...

In this work, we address these open questions by introducing a symmetry-preserving ansatz for learning problems where the training data is given in form of weighted graphs, and study its performance ...