{"ID":2847433,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.27165","arxiv_id":"2510.27165","title":"Structure-Aware Optimal Intervention for Rumor Dynamics on Networks: Node-Level, Time-Varying, and Resource-Constrained","abstract":"Rumor propagation in social networks undermines social stability and public trust, calling for interventions that are both effective and resource-efficient. We develop a node-level, time-varying optimal intervention framework that allocates limited resources according to the evolving diffusion state. Unlike static, centrality-based heuristics, our approach derives control weights by solving a resource-constrained optimal control problem tightly coupled to the network structure. Across synthetic and real-world networks, the method consistently lowers both the infection peak and the cumulative infection area relative to uniform and centrality-based static allocations. Moreover, it reveals a stage-aware law: early resources prioritize influential hubs to curb rapid spread, whereas later resources shift to peripheral nodes to eliminate residual transmission. By integrating global efficiency with fine-grained adaptability, the framework offers a scalable and interpretable paradigm for misinformation management and crisis response.","short_abstract":"Rumor propagation in social networks undermines social stability and public trust, calling for interventions that are both effective and resource-efficient. We develop a node-level, time-varying optimal intervention framework that allocates limited resources according to the evolving diffusion state. Unlike static, cen...","url_abs":"https://arxiv.org/abs/2510.27165","url_pdf":"https://arxiv.org/pdf/2510.27165v1","authors":"[\"Yan Zhu\",\"Qingyang Liu\",\"Chang Guo\",\"Tianlong Fan\",\"Linyuan Lü\"]","published":"2025-10-31T04:28:45Z","proceeding":"cs.SI","tasks":"[\"cs.SI\"]","methods":"[\"Diffusion Model\"]","has_code":false}