In the case of graphs, the probabilistic encoder model takes a graph G as input. From this input, qφ then defines a distribution qφ(Z|G) over latent representations. We need to specify the latent conditional distribution as Z ∼ N (µφ(G), σ(φ(G)), where µφ and σφ are neural networks that generate the mean and variance parameters for a normal distribution, from which we sample latent embeddings Z.