and an implementation to compute the vectors x and y of all nodes was done in Ref 54: Hence, a feature vector, z(v), is composed of the betweenness centrality, the in degree, the out degree, the pagerank, and the HITS y-hubs of a node. To prevent biased influences of the different metrics, we scale each feature between zero and one, using a maximum/minimum normalisation. For data compression and visualisation purposes, we reduce the dimensions of the input vectors. We performed a principal component analysis (PCA) based on a singular value decomposition as implemented in Ref 58 and obtain a reduced feature representation z'(v).