b4fd1d2cb085390fbbadae65e07876a7-Supplemental.pdf

The formulation is very similar to the method for learning positional node embeddings. Asynthetic molecular graph regression dataset, where thepredictedscore isgivenby the subtraction of computationally estimated propertieslogP SA. Thetask is to classify the nodes into 2 communities, testing the GNNs ability to recognize predetermined subgraphs. For the training parameters, we employed an Adam optimizer with alearning rate decay strategy initializedin{10 3,10 4}asper[15],withsomeminormodifications: ZINC[15]. We selected aninitial learning rateof7 10 4 and increased thepatiencefrom 10 to 25 to ensure convergence.