Graph generation by sequential edge prediction

Graph generation with Machine Learning models is a challenging problem with applications in various research fields. Here, we propose a recurrent Deep Learning based model to generate graphs by learning to predict their ordered edge sequence. Despite its simplicity, our experiments on a wide range of datasets show that our approach is able to generate graphs originating from very different distributions, outperforming canonical graph generative models from graph theory, and reaching performances comparable to the current state of the art on graph generation.