Current studies on explaining graph neural networks (GNNs) are proposed separately at the model or instance levels, each offering unique insights into GNNs’ prediction behaviors. Few studies have explored the potential of bridging multiple-level explainers to enhance explanation quality. To fill this gap, we propose IMOE, a simple yet effective framework that Incorporates Model-level explanations into the Optimization of instance-level Explainers. IMOE learns model-level explanations using GFlowNet. It further extracts representative prototype graph patterns with appropriate diversity via graph clustering, mitigating noise and computational overhead associated with large amounts of graph patterns. Moreover, IMOE leverages the prototype graph patterns to guide the optimization of the instance-level explainer, offering global information for learning faithful explanations. Experiments conducted on four datasets demonstrate the effectiveness and generalization of IMOE. Qualitative studies emphasize IMOE’s ability to generate explanations that align with human intuition and domain knowledge. Data and code are available at https://anonymous.4open.science/r/IMOE-0FFC