Work

Explicit Feature Interaction-Aware Graph Neural Network

Year: 2024

Type: article

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.

Source: IEEE Access

DOI: 10.1109/access.2024.3357887

Authors: Minkyu Kim, Hyun-Soo Choi, Jinho Kim

Institutions: Seoul National University of Science and Technology, Kangwon National University

Language: English

Topic: Advanced Graph Neural Networks

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,995

Funders: National Research Foundation, National Research Foundation of Korea, Ministry of Science and ICT, South Korea