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arXiv · 2204.03225

Explicit Feature Interaction-aware Graph Neural Networks

Minkyu Kim·Hyun-Soo Choi·Jinho Kim
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Abstract

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

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Minkyu Kim, Hyun-Soo Choi, Jinho Kim. 2022-04-07. Explicit Feature Interaction-aware Graph Neural Networks. https://doi.org/10.1109/access.2024.3357887

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