Data in tabular format is frequently occurring in real-world applications. Graph Neural Networks (GNNs) have recently been extended to effectively handle such data, allowing feature interactions to be captured through representation learning. However, these approaches essentially produce black-box models, in the form of deep neural networks, precluding users from following the logic behind the model predictions. We propose an approach, called IGNNet (Interpretable Graph Neural Network for tabular data), which constrains the learning algorithm to produce an interpretable model, where the model shows how the predictions are exactly computed from the original input features. A large-scale empirical investigation is presented, showing that IGNNet is performing on par with state-of-the-art machine-learning algorithms that target tabular data, including XGBoost, Random Forests, and TabNet. At the same time, the results show that the explanations obtained from IGNNet are aligned with the true Shapley values of the features without incurring any additional computational overhead.

翻译：表格格式的数据在现实应用中频繁出现。图神经网络（GNNs）最近被扩展以有效处理此类数据，通过表示学习捕捉特征交互。然而，这些方法本质上以深度神经网络的形式产生黑箱模型，妨碍用户理解模型预测背后的逻辑。我们提出一种名为IGNNet（面向表格数据的可解释图神经网络）的方法，该方法约束学习算法以生成可解释模型，其中模型展示了预测是如何从原始输入特征中精确计算得出的。通过大规模实证研究显示，IGNNet的性能与针对表格数据的最先进机器学习算法（包括XGBoost、随机森林和TabNet）相当。同时，结果表明，从IGNNet获得的解释与特征的真实Shapley值一致，且无需额外计算开销。