In various contexts, network embedding techniques have been developed forperforming analysis on a single graph. This approach has been proven to workfor different applications, such as node classification or link prediction. A goodnetwork embedding algorithm is capable to capture only the relevant features ofthe graph and to reproduce them in a low-dimensional Euclidean space.In the context of neuroscience, networks are currently used for representing thesystem of connections in the brain with the purpose of determining the charac-teristics of a pathological brain. However, discriminating a healthy human brain connectivity network from a clinical one using common network descriptors couldbe misleading. In fact, a difference in the currently used graph measures couldnot be detected or could be insufficient for the discrimination.