Should Graph Neural Networks Use Features, Edges, Or Both?
This work challenges the necessity of GNNs for graph classification, potentially impacting researchers and practitioners by suggesting simpler alternatives.
The paper investigates whether Graph Neural Networks (GNNs) are necessary for graph classification by analyzing the contributions of node features and edge information, finding that GNNs do not outperform simpler combinations of these components and that edge-only models may not generalize to GNNs.
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.