A New Concept for Explaining Graph Neural Networks

Graph neural networks (GNNs), similarly to other connectionist models, lack transparency in their decision-making. A number of sub-symbolic approaches, such as generating importance masks, have been developed to provide insights into the decision making process of such GNNs. These are first important steps on the way to model explainability, but leaving the interpretation of these sub-symbolic explanations to human analysts can be problematic since humans naturally rely on their background knowledge and therefore also their biases about the data and its domain. To overcome this problem we introduce a conceptual approach by suggesting model-level explanation rule extraction through a standard white-box learning method from the generated importance masks.