Explainer for GNN based on evolutionary ensemble learning algorithm
To address the challenge of limited explainability in Graph Neural Network(GNN)models,a novel explainer employing an evolutionary ensemble learning algorithm was developed.More refined explanations for model predictions compared to existing methods were offered.Both GNNExplainer and PGExplainer,recognized as leading tools in the field,were utilized as initial explainers.Guided by the primary explanation results for the design of genetic operators,an improved genetic algorithm was employed to integrate the initial explanations into a comprehensive final explanation.Extensive experiments were conducted on four real-world and four synthetic datasets,with results evaluated from both qualitative and quantitative perspectives.The experimental results showed that the proposed approach achieved an average improvement of 17%in accuracy and 20%in fidelity over baseline methods.Compared to conventional ensemble fusion strategies,an overall average improvement of 29%across all metrics was achieved by using improved genetic algorithm as an integrator.The substantial enhancement in GNN explanation capabilities was realized through the adoption of an evolutionary ensemble strategy.
graph neural networksevolutionary algorithmsensemble learningdeep learningmachine learningexplainable AI