All training data, model structures, and hyperparameters used in this study can be found in our Zenodo repository, from which all results can be reproduced when used in conjunction with the dGNN implementation built in Pytorch Nonetheless, our minimal complexity architecture consists of T = 2 convolution steps, v v v i ∈ R 8 , and v v v d ∈ R , leading to a GNN with only ∼ 2, 000 learnable parameters. After fixing the architecture, the learning rate was adjusted to minimize K-fold test set performance (next section), and over-fitting was minimized via early-stopping using the mean absolute error on a 10% validation set within each train fold.