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Critical and Steady State for Epidemic Dynamics on the Stationary Growth Networks

机译:静止增长网络对流行性动态的临界和稳定状态

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This paper discusses the dynamics of the epidemic spreading susceptible-infected-recovery (SIR) model on the stationary growth networks, relating them to the node-connectivity distribution that characterizes the network. We introduce the interaction Markov chains mean-field equations and the stochastic numerical approach to examine the threshold (steady state) and time-independent behaviour for the epidemic model on such network. Analytical methods and simulated experiments show there exhibits a critical threshold for the infinite size networks with the exponent less than or equal to 3 below which it cannot diffuse in such type of the system. For the BA networks, we present analytical and Monte Carlo calculations and compare the results with those obtained by the numerical method, which indicates stochastic numerical approach (SNA) can save memory and get the fast exploration.
机译:本文讨论了静止增长网络上的疫情扩展易感恢复(SIR)模型的动态,使它们与特征在于网络的节点连接分布。我们介绍了相互作用的马尔可夫链式平均方程和随机数值方法,以检查这些网络上的阈值(稳态)和时间无关行为。分析方法和模拟实验表明,具有指数小于或等于3的无限尺寸网络的临界阈值,下面不能以这种类型的系统扩散。对于BA网络,我们呈现分析和蒙特卡罗计算,并将结果与​​数字方法获得的结果进行比较,这表明随机数值方法(SNA)可以节省内存并获得快速探索。

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