在极化网络上使用Q-Neighbor Ising模型
Anna Chmiel1, Julian Sienkiewicz1
1Warsaw University of Technology, Faculty of Physics, Koszykowa 75, 00-662 Warszawa, Poland.
Physical review. E
|February 7, 2025
概括
在意见形成模型中,游说的大小 (q) 和图形重叠 (v) 相互作用,打破两极分化的意见. 特定的重叠水平 (v*) 破坏了集群,对q和温度有复杂的依赖.
科学领域:
- 统计力学 统计力学
- 计算社会科学 计算社会科学
- 网络科学 网络科学
背景情况:
- q-邻居Ising模型是研究论动态的框架.
- 了解网络结构如何影响意见两极分化至关重要.
- 之前的研究已经探讨了影响论形成的因素,但游说团体规模和图表重叠的相互作用需要进一步调查.
研究的目的:
- 在q-邻居Ising模型中,研究大厅大小 (q) 和图形重叠 (v) 之间的关系.
- 为了确定关键的重叠水平 (v*),扰乱不同大小的游说团体的意见两极分化.
- 分析温度对意见集群稳定性和两极分化的影响.
主要方法:
- 蒙特卡洛模拟被用来建模论动态.
- 用分析方法来支持模拟发现.
- 该研究系统地改变了大厅大小 (q) 和重叠 (v) 参数.
主要成果:
- 对于大小q≥3的游说团体,发现一个关键的重叠v*可以破坏两极分化的意见集群.
- v*对q的依赖是非常重要的,表现出与q的平价相关的振荡,特别是在零温度下.
- 温度上升通常会降低极化,并在较低的重叠值时崩集群.
结论:
- 游说的大小和图表重叠是对意见两极化稳定性的关键决定因素.
- q和v*之间的复杂关系凸显了意见动态中的细微影响.
- 温度起到破坏稳定的作用,尽管对特定参数范围观察到反直觉效应.
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