  Graph Neural Networks (GNNs) are widely adopted in advanced AI systems due to
their capability of representation learning on graph data. Even though GNN
explanation is crucial to increase user trust in the systems, it is challenging
due to the complexity of GNN execution. Lately, many works have been proposed
to address some of the issues in GNN explanation. However, they lack
generalization capability or suffer from computational burden when the size of
graphs is enormous. To address these challenges, we propose a multi-level GNN
explanation framework based on an observation that GNN is a multimodal learning
process of multiple components in graph data. The complexity of the original
problem is relaxed by breaking into multiple sub-parts represented as a
hierarchical structure. The top-level explanation aims at specifying the
contribution of each component to the model execution and predictions, while
fine-grained levels focus on feature attribution and graph structure
attribution analysis based on knowledge distillation. Student models are
trained in standalone modes and are responsible for capturing different teacher
behaviors, later used for particular component interpretation. Besides, we also
aim for personalized explanations as the framework can generate different
results based on user preferences. Finally, extensive experiments demonstrate
the effectiveness and fidelity of our proposed approach.
