  Graph neural networks (GNNs) have proven their efficacy in a variety of
real-world applications, but their underlying mechanisms remain a mystery. To
address this challenge and enable reliable decision-making, many GNN explainers
have been proposed in recent years. However, these methods often encounter
limitations, including their dependence on specific instances, lack of
generalizability to unseen graphs, producing potentially invalid explanations,
and yielding inadequate fidelity. To overcome these limitations, we, in this
paper, introduce the Auxiliary Classifier Generative Adversarial Network
(ACGAN) into the field of GNN explanation and propose a new GNN explainer
dubbed~\emph{ACGAN-GNNExplainer}. Our approach leverages a generator to produce
explanations for the original input graphs while incorporating a discriminator
to oversee the generation process, ensuring explanation fidelity and improving
accuracy. Experimental evaluations conducted on both synthetic and real-world
graph datasets demonstrate the superiority of our proposed method compared to
other existing GNN explainers.
