  With the growing use of deep learning methods, particularly graph neural
networks, which encode intricate interconnectedness information, for a variety
of real tasks, there is a necessity for explainability in such settings. In
this paper, we demonstrate the applicability of popular explainability
approaches on Graph Attention Networks (GAT) for a graph-based super-pixel
image classification task. We assess the qualitative and quantitative
performance of these techniques on three different datasets and describe our
findings. The results shed a fresh light on the notion of explainability in
GNNs, particularly GATs.
