论文标题
显式特征互动感知图形神经网络
Explicit Feature Interaction-aware Graph Neural Networks
论文作者
论文摘要
图形神经网络（GNN）是处理图形结构数据的强大工具。但是，他们的设计通常会限制他们仅学习高阶特征交互，而低阶功能相互作用被忽略了。为了解决这个问题，我们引入了一种新型的GNN方法，称为显式特征相互作用感知图神经网络（EFI-GNN）。与传统的GNN不同，EFI-GNN是一个多层线性网络，旨在在图表中明确对任意订单的特征交互进行建模。为了验证EFI-GNN的功效，我们使用各种数据集进行实验。实验结果表明，EFI-GNN具有现有GNN的竞争性能，当GNN与EFI-GNN共同训练时，预测性能会有所改善。此外，由于其线性结构，EFI-GNN的预测是可解释的。 EFI-GNN的源代码可从https://github.com/gim4855744/efi-gnn获得
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
