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

Should Graph Neural Networks Use Features, Edges, Or Both?