摘要
建立了一个具有连边记忆性的活跃驱动网络,其中节点在产生连边时会以一定的概率(记忆连接偏好)连向之前多个时间步内(记忆窗口)的邻居节点.进一步研究了模型的易感-感染-恢复(susceptibleinfected-recovery,SIR)传播动力学过程,然后基于淬火平均场方法推导出了流行病的暴发阈值.仿真发现,记忆性会降低网络的最大度而增加最大边权重,这常见于真实的移动电话网络数据集中.另外,对于传播动力学,流行阈值(最终暴发规模)会随着连接偏好和记忆窗口的增加而增大(减小).
In the active driven network established in this study with link memory,nodes connect to their neighbors from previous time steps(a memory window)with a certain probability(memory connection preference).Delving into the susceptible-infected-recovery(SIR)epidemic dynamics of the model,the epidemic outbreak threshold is derived using the quenched mean-field method,demonstrating that memory reduces the network’s maximum degree but increases the maximum edge weight,a pattern commonly observed in real-world mobile phone network datasets.Furthermore,in epidemic dynamics,with the rise in connection preference and memory window,the outbreak threshold increases,whereas final outbreak size decreases.
作者
李政宛
唐明
LI Zhengwan;TANG Ming(School of Physics and Electronic Science,East China Normal University,Shanghai 200241,China)
出处
《华东师范大学学报(自然科学版)》
北大核心
2025年第3期147-156,共10页
Journal of East China Normal University(Natural Science)
基金
国家自然科学基金(12231012,11975099)
国家自然科学基金-国际合作项目(82161148012)。
