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关于异质协同作用在复杂网络上的传染过程的影响的研究
Zixiang Yan1, Jian Gao1, Shengfeng Wang2
1School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Chaos (Woodbury, N.Y.)
|July 21, 2023
概括
个体异质性影响协同作用的传染动态. 在协同力量中引入权力法异质性改变了关键值,影响了复杂网络上的传染传播.
科学领域:
- 复杂的系统复杂的系统.
- 网络科学 网络科学
- 流行病学 流行病学
背景情况:
- 协同传染在自然和社会系统中普遍存在.
- 网络结构对传染的影响得到了充分的研究.
- 基于个体的异质性对协同传染的影响不太清楚.
研究的目的:
- 研究基于个体的异质性对协同传染的影响.
- 在复杂的网络中,将协同效应的强度作为个体程度的函数来建模.
- 分析由于异质协同作用而导致的传染阶段过渡的变化.
主要方法:
- 在易受感染易受感染模型中引入了基于个人的权力法异质性.
- 假设的协同效应强度因个人程度而异.
- 用于分析解决方案的异质平均场 (HMF) 近似方法.
- 通过蒙特卡洛模拟验证结果.
- 将模型与高阶相互作用的简化传染模型进行比较.
主要成果:
- 不同质的协同作用显著改变了不连续相变的临界值 (σc).
- 度增强的协同作用减少了σc,而度抑制的协同作用增加了它.
- 分析结果与蒙特卡洛模拟预测一致.
- 该模型显示了与简化传染模型的统计相似性.
结论:
- 基于个体的异质性是协同传染动态的关键因素.
- 开发的模型为了解异质网络上的传染提供了一个框架.
- 该模型在统计学上捕捉了HMF近似中的简单高阶相互作用.
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