  Despite recent progress in Graph Neural Networks (GNNs), explaining
predictions made by GNNs remains a challenging open problem. The leading method
independently addresses the local explanations (i.e., important subgraph
structure and node features) to interpret why a GNN model makes the prediction
for a single instance, e.g. a node or a graph. As a result, the explanation
generated is painstakingly customized for each instance. The unique explanation
interpreting each instance independently is not sufficient to provide a global
understanding of the learned GNN model, leading to a lack of generalizability
and hindering it from being used in the inductive setting. Besides, as it is
designed for explaining a single instance, it is challenging to explain a set
of instances naturally (e.g., graphs of a given class). In this study, we
address these key challenges and propose PGExplainer, a parameterized explainer
for GNNs. PGExplainer adopts a deep neural network to parameterize the
generation process of explanations, which enables PGExplainer a natural
approach to explaining multiple instances collectively. Compared to the
existing work, PGExplainer has better generalization ability and can be
utilized in an inductive setting easily. Experiments on both synthetic and
real-life datasets show highly competitive performance with up to 24.7\%
relative improvement in AUC on explaining graph classification over the leading
baseline.
