Given the key components and a set of training graphs {G1, .., Gn}, we can train a VAE model by minimizing the evidence likelihood lower bound (ELBO), as shown below. The basic idea is that we seek to maximize the reconstruction ability of our decoder—i.e., the likelihood term Eqθ(Z|Gi) [pθ(Gi|Z)]—while minimizing the KL-divergence between our posterior latent distribution qθ(Z|Gi) and the prior p(Z).