  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.
