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Stochastic Hybrid Rumor Control:A Data-Driven Ensemble Learning Control Algorithm 认领 引用
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作者 ZHONG Xiaojing ZENG Jiaxin +3 位作者 XIANG Wendi CARABALLO Tomás DENG Feiqi PENG Yuqing 《Journal of Systems Science & Complexity》 SCIE EI CSCD 2026年第4期1436-1462,共27页
To explore the impact of various groups and methods on rumor propagation,the authors propose a‘Double-Refutation(DR)and Double-Blocking(DB)'rumor control strategy.This strategy combines external refutation via me... To explore the impact of various groups and methods on rumor propagation,the authors propose a‘Double-Refutation(DR)and Double-Blocking(DB)'rumor control strategy.This strategy combines external refutation via media reports,internal refutation by counteracting individuals,and both continuous and impulse blocking methods.By leveraging multi-synergy and aiming to minimize control costs,the authors propose stochastic optimal hybrid control strategies for rumor containment.Additionally,to enhance the response speed of the control strategy,the authors introduce an ensemble learning algorithm as a substitute for theoretical solutions.Numerical simulations demonstrate that the trained ensemble learning control algorithm can quickly identify sub-optimal control strategies for rumor spreading,with costs only 4.1%higher than those of the optimal control theory. 展开更多
关键词 Double refutation and blocking control mechanism dual-layer propagation model hybrid optimal control stacking ensemble learning control algorithm stochastic control
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