u are the the edge, node and global update functions. Here, as proposed by , all of them correspond to a single hidden-layer neural network with the ReLU activation function. Graph normalization is applied to the nodes updated of each graph to improve the generalization and convergence of the GNN during training. Finally, a global pooling is applied to each normalized graph to get the vectorial representation of the graph. Here, the sum global pooling layer was used.