Constructing enterprise talent heterogeneous information networks for key talent identification

Abstract

In organizational networks, where employee performance is dependent on strategic positioning and collaborative relationships across diverse workplace ecosystems, traditional enterprise talent identification systems fall short in capturing complex multi-relational dynamics. In order to accurately identify key talent through meta-path guided feature extraction and attention-based embedding mechanisms, this research suggests a Heterogeneous Information Network (HIN) framework that uses Graph Neural Networks (GNNs) to model employees, projects, departments, and skills as interconnected entities. The approach uses Heterogeneous Graph Attention Networks (HAN) for talent assessment and combines attribute-driven performance indicators, structural centrality measures, and semantic relationship patterns into a single learning framework. Compared to traditional Human Resource (HR) methods, which scored 72% precision and 68% recall, the experimental evaluation, which used enterprise data with 2,847 employees across 156 departments, shows improvements over current approaches, achieving 91% precision and 89% recall with a Normalized Discounted Cumulative Gain (NDCG) of 0.834. With domain expert validation confirming 87% agreement between algorithmic recommendations and professional assessments, the framework identifies high-potential employees who exhibit knowledge brokerage roles and cross-functional collaboration capabilities that traditional performance metrics overlook. With implications for strategic human capital optimization, these contributions position HINs as a paradigm shift for enterprise talent management.