Patel, HarshSahni, Shivam2025-08-282025-08-282022-11-01https://arxiv.org/abs/2211.01770https://d8.irins.org/handle/IITG2025/19833With 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.en-USGraph neural networksGATExplainability methodsImage classificationPattern recognitione-Printe-Print123456789/435