This talk will cover recent work related to training and evaluating graph ML models on synthetic graphs. First, we discuss GraphWorld, a framework and package for generating a high-diversity set of medium-scale graphs for finding edge-cases of GNN performance. Second, we discuss new graph generative models that have been added to GraphWorld since its release. Finally, we discuss more recent work on generating large, individual synthetic graphs, and the challenges involved in training a GNN model on such graphs.