A Survey on Explainability of Graph Neural Networks

Understand

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.