Employee turnover poses persistent challenges for organizations, disturbing operational continuity and strategic planning. Traditional machine learning models, while effective to a certain degree, often fail to capture relational and temporal patterns inherent in workforce dynamics. This study presents a graph-based simulation framework that models employee relationships as a network and applies Graph Neural Network (GNN) architectures, including Graph Attention Networks (GANs), graph sample and aggregate (namely GraphSAGE), and Graph Convolutional Networks (GCNs), to learn interdependencies influencing attrition. A stacked ensemble approach integrates these models using logistic regression to enhance predictive robustness. Evaluated on the IBM HR Analytics dataset, the proposed framework achieves 93.2% accuracy. To support managerial decision-making, explainable methods are used to identify critical risk factors such as job satisfaction, workload, and environmental conditions. This process-oriented modeling approach simulates employee attrition dynamics and generates interpretable outputs that guide proactive retention strategies.