  In this work, we propose a methodology for investigating the use of semantic
attention to enhance the explainability of Graph Neural Network (GNN)-based
models. Graph Deep Learning (GDL) has emerged as a promising field for tasks
like scene interpretation, leveraging flexible graph structures to concisely
describe complex features and relationships. As traditional explainability
methods used in eXplainable AI (XAI) cannot be directly applied to such
structures, graph-specific approaches are introduced. Attention has been
previously employed to estimate the importance of input features in GDL,
however, the fidelity of this method in generating accurate and consistent
explanations has been questioned. To evaluate the validity of using attention
weights as feature importance indicators, we introduce semantically-informed
perturbations and correlate predicted attention weights with the accuracy of
the model. Our work extends existing attention-based graph explainability
methods by analysing the divergence in the attention distributions in relation
to semantically sorted feature sets and the behaviour of a GNN model,
efficiently estimating feature importance. We apply our methodology on a lidar
pointcloud estimation model successfully identifying key semantic classes that
contribute to enhanced performance, effectively generating reliable post-hoc
semantic explanations.
