  Graph Neural Networks (GNNs) are the first choice for learning algorithms on
graph data. GNNs promise to integrate (i) node features as well as (ii) edge
information in an end-to-end learning algorithm. How does this promise work out
practically? In this paper, we study to what extend GNNs are necessary to solve
prominent graph classification problems. We find that for graph classification,
a GNN is not more than the sum of its parts. We also find that, unlike
features, predictions with an edge-only model do not always transfer to GNNs.
