Graph Neural Networks (GNNs) have emerged as the predominant approach for
learning over graph-structured data. However, most GNNs operate as black-box
models and require post-hoc explanations, which may not suffice in high-stakes
scenarios where transparency is crucial. In this paper, we present a GNN that
is interpretable by design. Our model, Graph Neural Additive Network (GNAN), is
a novel extension of the interpretable class of Generalized Additive Models,
and can be visualized and fully understood by humans. GNAN is designed to be
fully interpretable, allowing both global and local explanations at the feature
and graph levels through direct visualization of the model. These
visualizations describe the exact way the model uses the relationships between
the target variable, the features, and the graph. We demonstrate the
intelligibility of GNANs in a series of examples on different tasks and
datasets. In addition, we show that the accuracy of GNAN is on par with
black-box GNNs, making it suitable for critical applications where transparency
is essential, alongside high accuracy.

该研究论文介绍了一种可解释性图神经网络模型，名为 Graph Neural Additive Network（GNAN），通过直接可视化模型，可以在特征和图层次上提供全局和局部解释，展现模型在目标变量、特征和图之间的关系，实验证明 GNAN 与黑盒图神经网络在准确性上相当，因此适用于需要透明度的关键应用。