Assuming one-dimensional node features for now, the output of each layer is a vector of node representations h ( k ) h^{(k)} h(k), where each node’s representation corresponds to a row of the vector. A simpler way to incorporate graph-level information is to compute embeddings of the entire graph by pooling node (and possibly edge) embeddings, and then using the graph embedding to update node embeddings, following an iterative scheme similar to what we have looked at here. Graph Classification : By aggregating node representations, one can construct a vector representation of the entire graph.