ABSTRACT With the proliferation of mobile social networks, controlling information dissemination faces challenges in dynamic environments where state transition models are often unavailable. To address the optimal control problem under such model‐free constraints, this paper aims to minimize the cumulative cost of epidemic‐based information dissemination by innovatively integrating temporal difference (TD) learning with real‐time capacity adaptation. The proposed method dynamically identifies optimal control signal timing, eliminating dependency on predefined state matrices. Experimental results demonstrate a 7.0% reduction in cumulative network cost and a 52.6% improvement in s‐controllability compared to dynamic programming baselines. This model‐free framework not only enhances robustness in time‐varying networks but also offers scalability for large‐scale applications, advancing real‐time control in social media analysis and public opinion management.
Xia et al. (Thu,) studied this question.