The basic Generative Adversarial Network (GAN) based approach to graph generation is similar to graph-level Variational Autoencoders (VAEs). For the generator, a simple multi-layer perceptron (MLP) can be employed to generate a matrix of edge probabilities given a seed vector $z$: $\tilde{A}=\sigma(MLP(z))$. Given this matrix, a discrete adjacency matrix $\hat{A} \in Z^{|V| \times |V|}$ is generated by sampling independent Bernoulli variables for each edge, where probabilities are given by the entries of $\tilde{A}$. For the discriminator, any Graph Neural Network (GNN) based graph classification model can be employed. The generator and discriminator models are then trained using standard tools for GAN optimization.