发表机构
Posts and Telecommunications Institute of Technology（邮电大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对SDIR模型，通过分析D和I状态向量动力学提出更紧致的上界，改进谱半径收敛条件，并在合成与真实网络上验证了影响力最小化的有效性。
AI 中文摘要
信息扩散可以通过限制或移除在线社交网络以及现实世界网络中的链接（边）来控制。为了识别在最小化扩散的同时要移除的最有影响力的链接，以往研究提出了SIR和SIS模型中传播过程的上界，利用超模性和加权矩阵来识别接触网络中的关键链接。然而，在某些情况下，现有的上界不够紧致，无法准确捕捉重要边的影响，例如[14]（Khanh-Cho-Dung，第40届ICOIN会议论文集，2026年）中的SDIR模型。因此，我们通过直接在$2N$维空间中分析状态向量D和I的动力学，提出了一个更紧致的上界来控制SDIR模型中的扩散。该方法产生了改进的谱半径收敛条件，并优于先前的方法。在合成Erdos-Renyi网络和真实世界Haslemere数据集上使用贪心边删除算法的模拟，证明了其在社交网络上影响力最小化的有效性。
英文摘要
Information diffusion can be controlled by restricting or removing links (edges) in online social networks, as well as in real-world networks. To identify the most influential links to remove while minimizing diffusion, previous studies have proposed upper bounds for spreading processes in SIR and SIS models, using supermodularity and weighted matrices to identify critical links in contact networks. However, in some cases, existing upper bounds are not sufficiently tight to accurately capture the effect of important edges, as in the SDIR model of [14] (Khanh-Cho-Dung, Proceedings of 40th ICOIN, 2026). We therefore propose a tighter upper bound for controlling diffusion in the SDIR model by directly analyzing the dynamics of the two state vectors D and I in a $2N$-dimensional space. This approach yields an improved spectral-radius convergence condition and outperforms the previous method. Simulations on the synthetic Erdos-Renyi network and the real-world Haslemere dataset using a Greedy edge-deletion algorithm demonstrate its effectiveness for influence minimization on social networks.
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