  Graph neural networks (GNNs) are powerful tools for handling graph-structured
data. However, their design often limits them to learning only higher-order
feature interactions, leaving low-order feature interactions overlooked. To
address this problem, we introduce a novel GNN method called explicit feature
interaction-aware graph neural network (EFI-GNN). Unlike conventional GNNs,
EFI-GNN is a multilayer linear network designed to model arbitrary-order
feature interactions explicitly within graphs. To validate the efficacy of
EFI-GNN, we conduct experiments using various datasets. The experimental
results demonstrate that EFI-GNN has competitive performance with existing
GNNs, and when a GNN is jointly trained with EFI-GNN, predictive performance
sees an improvement. Furthermore, the predictions made by EFI-GNN are
interpretable, owing to its linear construction. The source code of EFI-GNN is
available at https://github.com/gim4855744/EFI-GNN
