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arXiv 2409.05063eess.SYcs.SY
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关于具有部分固执社区的随机图上 Friedkin-Johnsen 模型最终观点的研究

On final opinions of the Friedkin-Johnsen model over random graphs with partially stubborn community

Lingfei Wang, Yu Xing, Karl H. Johansson

AI总结:

该文研究 Bernoulli 随机图上含部分固执社区的 FJ 观点动力学,证明最终观点向期望图结果集中并给出概率界,实验验证了跨社区链路概率与固执程度的影响。

摘要:

本文研究了具有一个由部分固执智能体组成的社区的 Friedkin-Johnsen(FJ)模型的最终观点形成。FJ 模型的底层网络是对称的,并由随机图模型生成,其中每条链路都独立地按照 Bernoulli 分布添加。结果表明,在固执智能体与其他智能体连接良好的条件下,随着网络规模增大,FJ 模型的最终观点将集中于期望图上 FJ 模型的最终观点附近。针对存在非固执智能体和不存在非固执智能体两种情形,分别给出了这两个最终观点向量之间距离的概率界。文中提供了数值实验以说明理论发现。仿真表明,在存在非固执智能体时,固执社区与非固执社区之间的链路概率会显著影响两个最终观点向量之间的距离。此外,如果所有智能体都是固执的,则观点距离会随智能体固执程度的增加而减小。

英文摘要:

This paper studies the formation of final opinions for the Friedkin-Johnsen (FJ) model with a community of partially stubborn agents. The underlying network of the FJ model is symmetric and generated from a random graph model, in which each link is added independently from a Bernoulli distribution. It is shown that the final opinions of the FJ model will concentrate around those of an FJ model over the expected graph as the network size grows, on the condition that the stubborn agents are well connected to other agents. Probability bounds are proposed for the distance between these two final opinion vectors, respectively for the cases where there exist non-stubborn agents or not. Numerical experiments are provided to illustrate the theoretical findings. The simulation shows that, in presence of non-stubborn agents, the link probability between the stubborn and the non-stubborn communities affect the distance between the two final opinion vectors significantly. Additionally, if all agents are stubborn, the opinion distance decreases with the agent stubbornness.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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