Recently, many post-hoc GNN explanation methods have been explored to uncover GNNs'
predictive behaviors by analyzing the embeddings produced by the GNN models. However …

As Graph Neural Networks (GNNs) have been widely used in real-world applications, model
explanations are required not only by users but also by legal regulations. However …

Numerous explainability techniques have been developed to reveal the prediction principles
of Graph Neural Networks (GNNs) across diverse domains. However, many existing …

Abstract Model-level Graph Neural Network (GNN) explanation methods have become
essential for understanding the decision-making processes of GNN models on a global …

Understanding and explaining the predictions of Graph Neural Networks (GNNs), is crucial
for enhancing their safety and trustworthiness. Subgraph-level explanations are gaining …

Instance-level explanation of graph neural networks (GNNs) is a well-studied area. These
explainers, however, only explain an instance (eg, a graph) and fail to uncover the …