  Graph Neural Networks (GNNs) have become increasingly ubiquitous in numerous
applications and systems, necessitating explanations of their predictions,
especially when making critical decisions. However, explaining GNNs is
challenging due to the complexity of graph data and model execution. Despite
additional computational costs, post-hoc explanation approaches have been
widely adopted due to the generality of their architectures. Intrinsically
interpretable models provide instant explanations but are usually
model-specific, which can only explain particular GNNs. Therefore, we propose a
novel GNN explanation framework named SCALE, which is general and fast for
explaining predictions. SCALE trains multiple specialty learners to explain
GNNs since constructing one powerful explainer to examine attributions of
interactions in input graphs is complicated. In training, a black-box GNN model
guides learners based on an online knowledge distillation paradigm. In the
explanation phase, explanations of predictions are provided by multiple
explainers corresponding to trained learners. Specifically, edge masking and
random walk with restart procedures are executed to provide structural
explanations for graph-level and node-level predictions, respectively. A
feature attribution module provides overall summaries and instance-level
feature contributions. We compare SCALE with state-of-the-art baselines via
quantitative and qualitative experiments to prove its explanation correctness
and execution performance. We also conduct a series of ablation studies to
understand the strengths and weaknesses of the proposed framework.
