So Graph Neural Networks is about representation learning where initially representation of graph is learned in the form of node embeddings. My question is: Are the output values back propagated and influence learned node embeddings? The paper I read seems to not account for output being backpropogated so that embeddings get learned. So does that mean learning node embeddings is agnostic of output values during training?



You must log in to answer this question.