  Graph Neural Networks (GNNs) are deep learning models that take graph data as
inputs, and they are applied to various tasks such as traffic prediction and
molecular property prediction. However, owing to the complexity of the GNNs, it
has been difficult to analyze which parts of inputs affect the GNN model's
outputs. In this study, we extend explainability methods for Convolutional
Neural Networks (CNNs), such as Local Interpretable Model-Agnostic Explanations
(LIME), Gradient-Based Saliency Maps, and Gradient-Weighted Class Activation
Mapping (Grad-CAM) to GNNs, and predict which edges in the input graphs are
important for GNN decisions. The experimental results indicate that the
LIME-based approach is the most efficient explainability method for multiple
tasks in the real-world situation, outperforming even the state-of-the-art
method in GNN explainability.
