Intelligent regulation of university faculty interdisciplinary collaboration networks based on complex network topology evolution and stochastic differential equations.

This study develops a comprehensive theoretical framework integrating complex network topology evolution with stochastic differential equation modeling to characterize and intelligently regulate interdisciplinary collaboration dynamics among university faculty, addressing personnel establishment verification and classified management challenges. The proposed approach addresses persistent barriers in academic collaboration by combining discrete network structural changes with continuous collaboration intensity dynamics under stochastic perturbations. The framework incorporates heterogeneous faculty characteristics, multi-dimensional collaboration attributes, and reinforcement learning-based intelligent regulation mechanisms for dynamic network optimization. Experimental validation demonstrates superior performance compared to conventional methods, achieving 89.3% prediction accuracy and significant improvements in network efficiency, collaboration diversity, and resource utilization. The intelligent regulation mechanism successfully enhances interdisciplinary bridge formation by 34% while maintaining structural stability across diverse institutional scenarios. The mathematical framework captures both deterministic trends and random fluctuations inherent in real-world collaboration systems, providingacademic administrators with quantitative tools for optimizing university faculty collaboration networks through classified management approaches and enhancing research productivity while supporting personnel establishment verification processes. Results confirm robust scalability properties and adaptability to varying environmental conditions, making the approach suitable for practical implementation in academic institutions with different organizational structures and operational constraints.