In Chapter 6.6, it says “The inference algorithm is different from the training algorithm, as the representations of all nodes should be computed layer by layer, starting from the first layer. The consequence is that the inference algorithm will have an outer loop iterating over the layers, and an inner loop iterating over the minibatches of nodes. In contrast, the training algorithm has an outer loop iterating over the minibatches of nodes, and an inner loop iterating over the layers for both neighborhood sampling and message passing.”
I was wondering why the inference algorithm should be different from the training algorithm? If the inference procedure goes the same as training, what are the disadvantages?