Graph neural networks generalize the convolutional operator from computer vision to irregular domains in a mechanism generally referred to as neighborhood aggregation or message passing . With x k-1 i ∈ R F denoting the node features of node i in layer (k -1) and e (i,j) ∈ R D denoting the optional edge features from node i to node j, message-passing graph neural networks can be described as