A Theory |S, Y, G) EQ [ln P (G

In this section, we provide more details of model implementation and experiment setup for reproducibility of the experimental results. B.1 Details of Model Implementation B.1.1 Details of the Prediction Model The prediction model f is implemented with a graph neural network based model. Specifically, this prediction model includes the following components: Three layers of graph convolutional network (GCN) [34] with learnable node masks. The prediction model uses negative log likelihood loss. The representation dimension is set as 32. We use Adam optimizer, set the learning rate as 0.001, weight decay as 1e 5, the training epochs as 600, dropout rate as 0.1, and batch size as 500.