Unveiling the core-periphery dynamics and driving factors of intercity scientific mobility in China

In China's uneven innovation landscape, intercity researcher mobility shapes urban knowledge hierarchies. Using ORCID data, this study reconstructs a national mobility network and applies the Weighted Degree-Corrected Stochastic Block Model (WDSBM) to reveal a hierarchical core–periphery structure. A small number of high-flow corridors dominate national exchanges, reinforcing spatial polarization. Combining XGBoost with SHAP interpretation, nonlinear drivers—including academic prestige, economic scale, housing costs, and geographic distance—are identified, and key interaction effects are revealed. Notably, elite universities exert stronger attraction when paired with high GDP, while high housing costs deter mobility even in high-income cities unless offset by institutional strength. Distance remains a constraint, but its effect is mitigated in cities with strong academic or economic capacity. These findings highlight that mobility is shaped not by single factors but by their structural alignment. The study offers a hybrid analytical framework linking network position with behavioral drivers, providing actionable insights for place-sensitive, tiered talent policies aimed at promoting inclusive and efficient innovation systems.