In Neurosciences, networks are currently used for representing the brain connections system with the purpose of determining the specific characteristics of the brain itself. However, discriminating between a healthy human brain network and a pathological one using common network descriptors could be misleading. For this reason, we explored network embedding techniques with the purpose of brain connectivity networks comparison. We proposed first the definition of representative graph for healthy brain connectivity. Then, two classification procedures through embedding are introduced, achieving good accuracy results in different datasets. Moreover, the intriguing power of this technique is given by the possibility of visualizing networks in a low-dimensional space, facilitating the interpretation of the differences between networks under diverse conditions e.g. normal or pathological.