The base architecture of the GNNs consisted of three convolutional layers (GCN or GAT) alternated with ReLU functions, followed by a pooling layer and a single fully connected layer. Each hidden layer had 256 neurons. The models were trained for 400 epochs with a learning rate of 0.001 and a batch size of 64. Hyperparameters were not optimized. PyTorch was used together with the PyTorch Geometric library to create and train the GNNs. A gradient boosted tree model was trained using the xgboost package with default parameters and the same data split.