Random Mask Perturbation Based Explainable Method of Graph Neural Networks

Graph Neural Networks (GNNs) have garnered considerable attention due to their potential applications across multiple domains. However, enhancing their interpretability is a significant challenge for the crucial application. This paper proposes an innovative node perturbation-based method to explicate GNNs and unveil their decision-making processes. Categorized as a black-box method, it generates explanations of node importance solely through the input-output analysis of the model, obviating the necessity for internal access. The method employs fidelity as a metric for calculating the significance of perturbation masks and utilizes a sparsity threshold to filter the computation results. Furthermore, recognizing the impact of different node combinations on model prediction outcomes, we treat the mask as a random variable. By randomly sampling various masks, we compute perturbed node importance, facilitating the generation of user-friendly explanations. Comparative experiments and ablation studies conducted on both real and synthetic datasets substantiate the efficacy of our approach in interpreting GNNs. Additionally, through a case study, we visually demonstrate the method’s compelling interpretative evidence regarding model prediction outcomes.