@article{Li2026, 
author = {Liqin Li},
title = {Employee Relationship Network Modeling and Turnover Prediction Based on Graph Neural Networks},
year = {2026},
journal = {Complex System Modeling and Simulation},
volume = {6},
number = {3},
pages = {313-327},
keywords = {employee turnover prediction, Graph Neural Networks (GNN), ensemble learning, explainable Artificial Intelligence (XAI), human resource analytics},
url = {https://www.sciopen.com/article/10.23919/CSMS.2025.0035},
doi = {10.23919/CSMS.2025.0035},
abstract = {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.}
}