  With the rapid deployment of graph neural networks (GNNs) based techniques
into a wide range of applications such as link prediction, node classification,
and graph classification the explainability of GNNs has become an indispensable
component for predictive and trustworthy decision-making. Thus, it is critical
to explain why graph neural network (GNN) makes particular predictions for them
to be believed in many applications. Some GNNs explainers have been proposed
recently. However, they lack to generate accurate and real explanations. To
mitigate these limitations, we propose GANExplainer, based on Generative
Adversarial Network (GAN) architecture. GANExplainer is composed of a generator
to create explanations and a discriminator to assist with the Generator
development. We investigate the explanation accuracy of our models by comparing
the performance of GANExplainer with other state-of-the-art methods. Our
empirical results on synthetic datasets indicate that GANExplainer improves
explanation accuracy by up to 35\% compared to its alternatives.
