Generative Explanations for Graph Neural Network: Methods and
  Evaluations
Jialin Chen Kenza Amara Junchi Yu Rex Ying
Graph Neural Networks (GNNs) achieve state-of-the-art performance in various
graph-related tasks. However, the black-box nature often limits their
interpretability and trustworthiness. Numerous explainability methods have been
proposed to uncover the decision-making logic of GNNs, by generating underlying
explanatory substructures. In this paper, we conduct a comprehensive review of
the existing explanation methods for GNNs from the perspective of graph
generation. Specifically, we propose a unified optimization objective for
generative explanation methods, comprising two sub-objectives: Attribution and
Information constraints. We further demonstrate their specific manifestations
in various generative model architectures and different explanation scenarios.
With the unified objective of the explanation problem, we reveal the shared
characteristics and distinctions among current methods, laying the foundation
for future methodological advancements. Empirical results demonstrate the
advantages and limitations of different explainability approaches in terms of
explanation performance, efficiency, and generalizability.