with µ = GCN µ,n (X, A) and log σ = GCN σ,n (X, A). Here, GCN •,n is a graph convolutional neural network with n layers, X is a node feature matrix, A is the adjacency matrix of the graph, and N denotes the Gaussian distribution. Moreover, the model typically assumes the existence of self-loops, i.e. the diagonal of the adjacency matrix consists of ones.