I have worked through the “Node Classification with Graph Neural Networks” Example Notebook of Pytorch Geometric. In the further Example Section it was recommended to use data.val_mask of the Cora Set to increase the Model Accuracy by “selecting and testing the model with the highest validation performance”.

I am still trying to wrap my head around this idea, as only K-Fold Cross Validation and Early Stopping are known Techniques to me. Would love to get some guidance on where to start / what could be meant in the context of this Starter Exercise

Things I have already done:

• extended test function to apply (mask)
• implemented early stopping, based on validation loss