  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.
