Fig. 8.

Visualization of four explainers from the G-XAI Bench library on the BA-Shapes dataset. The visualization is for explaining the prediction of node u. We show the L + 1-hop around node u, where L is the number of layers of the GNN model predicting on the dataset. Two color bars indicate the intensity of attribution scores for the node and edge explanations. Note that edge importance is not defined for every method, so edges are set to black to indicate that the method does not provide edge scores. Visualization tools are a native part of the GraphXAI package, including user-friendly functions graphxai.Explanation.visualize_node and graphxai.Explanation.visualize_graph to visualize GNN explanations. The visualization tools in GraphXAI allow users to compare the explanations of different GNN explainers, such as gradient-based methods (Gradient and Grad-CAM) and perturbation-based methods (GNNExplainer and SubgraphX).