  Graph neural networks (GNNs) are powerful graph-based deep-learning models
that have gained significant attention and demonstrated remarkable performance
in various domains, including natural language processing, drug discovery, and
recommendation systems. However, combining feature information and
combinatorial graph structures has led to complex non-linear GNN models.
Consequently, this has increased the challenges of understanding the workings
of GNNs and the underlying reasons behind their predictions. To address this,
numerous explainability methods have been proposed to shed light on the inner
mechanism of the GNNs. Explainable GNNs improve their security and enhance
trust in their recommendations. This survey aims to provide a comprehensive
overview of the existing explainability techniques for GNNs. We create a novel
taxonomy and hierarchy to categorize these methods based on their objective and
methodology. We also discuss the strengths, limitations, and application
scenarios of each category. Furthermore, we highlight the key evaluation
metrics and datasets commonly used to assess the explainability of GNNs. This
survey aims to assist researchers and practitioners in understanding the
existing landscape of explainability methods, identifying gaps, and fostering
further advancements in interpretable graph-based machine learning.
