Creation and application of graph models for employee recruitment tasks

This study presents a mathematical hiring model based on the integration of graph methods and blockchain verification. Unlike traditional recruitment approaches, the proposed model incorporates candidates’ network characteristics, such as degree centrality, betweenness centrality, and PageRank, while also utilizing trust scores (Trust Score) to predict hiring success. A cluster analysis of interactions between candidates, employers, HR platforms, and certification centers was conducted, revealing key patterns in professional network formation. The scientific novelty of this research lies in the development of a comprehensive personnel selection algorithm that, in addition to standard ­HR metrics, employs graph-based indicators and blockchain verification mechanisms to enhance transparency and accuracy in hiring. For the first time, a methodology for calculating trust scores based on network analysis has been proposed, allowing for a more objective decision-making process in ­HR analytics. The results indicate that the application of graph methods in recruitment reduces hiring time by 32 %, decreases the likelihood of mismatched hires by 18 %, and improves candidate success prediction accuracy to 85 %. These findings confirm the potential of integrating graph neural networks and blockchain verification in automated HR systems.