Deep graph learning models have recently been developed to learn from various graphs that are prevalent in describing and modeling complex systems, including those in bioinformatics. However, a versatile explanation method for uncovering the general graph patterns that guide deep graph models in making predictions remains elusive. In this paper, we propose DGX, a novel deep graph model explainer that generates explanatory graphs to explain trained, black-box deep graph models. Its effectiveness is demonstrated by producing multiple graphs that collectively encode the structural knowledge captured by the graph neural network on both synthetic and real graph data. Importantly, DGX can produce diverse explanations by generating a set of distinguishable graphs and can provide customized explanations based on prior knowledge or constraints specified by users. We apply DGX to explain a mutagenicity prediction model by exploring the underlying groups of mutagenic compounds, and we explain the model on brain functional networks by revealing the structural patterns that enable the model to differentiate autism spectrum disorder from healthy controls. These findings offer an effective, diverse, and customized approach to explaining the underlying mechanisms and enhancing the understanding of models learned from real graph data, particularly in fields such as biomedicine and bioinformatics.
Hu et al. (Wed,) studied this question.