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, offering both global and local explanations at the feature and graph levels through direct visualization of the model. These visualizations describe exactly how 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.

翻译：图神经网络已成为处理图结构数据的主流方法。然而，大多数图神经网络作为黑盒模型运行，需要事后解释，这在透明度至关重要的高风险场景中可能不够充分。本文提出一种设计上可解释的图神经网络。我们的模型——图神经可加网络——是可解释广义可加模型类别的新颖扩展，可通过可视化方式被人类完全理解。该网络具备完全可解释性，通过模型直接可视化在特征和图层面提供全局与局部解释。这些可视化精确描述了模型如何利用目标变量、特征和图结构之间的关系。我们通过不同任务和数据集的系列案例展示了图神经可加网络的可理解性。此外，实验表明该模型的准确性与黑盒图神经网络相当，使其在需要高透明度与高精度的关键应用中具有实用价值。