Recent advances in Graph Neural Networks (GNN) have shown a dramatic improvement in computer networks problems. As a result, GNN seems promising to solve many relevant network optimization problems (e.g., routing and wavelength assignment) in self-driving software-defined networks. However, most state-of-the-art GNN-based networking techniques fail to generalize, which means that they perform well in network topologies seen during training, but not over large topologies. The reason behind this important limitation is that existing GNN networking solutions use standard graph neural networks that are not suited to learn large graph-structured information in routing purposes. In this Thesis, we transform the RWA problem into an ML-based classification problem, where the routing solution is provided by a classifier in response to a given input graph.