Post-hoc explainability of graph neural networks: A comprehensive survey
Wenzheng Ma, Xiaofeng Liu, Yihu Liu, Yinglong Ma · Information Sciences · 2026
Graph Neural Networks (GNNs) are powerful tools for analyzing graph-structured data and are widely applied in areas such as molecular structure prediction and social network analysis. However, GNN models are inherently nonlinear and opaque, making their internal mechanisms and the rationale behind their predictions difficult to understand. To address this issue, numerous explainability methods have been proposed to uncover the underlying decision-making mechanisms of GNNs. Among these, post-hoc explanation techniques offer significant flexibility, as they can be applied to any pre-trained GNN model without requiring modifications to the model itself. In this paper, we provide a comprehensive survey of existing post-hoc explainability methods for GNNs and propose a technology-oriented taxonomy based on the theoretical techniques they rely on. We analyze the strengths and limitations of each method and review commonly used datasets and evaluation protocols in the field. We further conduct a quantitative comparison of representative methods on selected datasets using commonly adopted evaluation metrics. Moreover, we outline promising research directions to advance the field. Altogether, this survey aims to provide researchers with a comprehensive understanding of the current landscape of post-hoc GNN explainability methods, identify their technical limitations, and facilitate the further advancement of explainable graph-based machine learning.