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arXiv 1810.04880physics.soc-phcs.SI
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网络定位不受突发感染的影响

Network localization is unalterable by infections in bursts

Qiang Liu, Piet Van Mieghem

备注：
11 pages (6 pages for main text and 5 pages for appendix), 9 figures (2 figures in main text and 7 figures in appendix) and 1 table in appendix; Modifications made in the main text: simulations added (Sec. 4.2; In Fig.2(b), the original networks with $γ=3$ in Version 1 around 10^2 and 10^3 have lost and this two networks are regenerated), references added. No changes in the appendix
发表信息：
IEEE Transactions on Network Science and Engineering, Vol. 6 (4), pp. 983 - 989, Oct.-Dec. 1 2019
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摘要：

为了揭示疾病局部化的现象，我们研究了一个突发的易感-感染-易感（SIS）模型，并在平均场近似下对该模型进行了分析。在突发SIS模型中，被感染节点周期性地感染所有邻居节点，并且近阈值稳态流行率是非常数的，最大化因子等于网络邻接矩阵的最大特征值$λ_1$.我们发现，最大近阈值流行率的突发SIS过程中的本地化网络趋于零，即使$λ_1$发散的热力学极限，这表明，爆发的感染不能把本地化的传播到一个离域的传播。我们的结果进行了评估合成和真实的网络。

英文摘要：

To shed light on the disease localization phenomenon, we study a bursty susceptible-infected-susceptible (SIS) model and analyze the model under the mean-field approximation. In the bursty SIS model, the infected nodes infect all their neighbors periodically, and the near-threshold steady-state prevalence is non-constant and maximized by a factor equal to the largest eigenvalue $λ_1$ of the adjacency matrix of the network. We show that the maximum near-threshold prevalence of the bursty SIS process on a localized network tends to zero even if $λ_1$ diverges in the thermodynamic limit, which indicates that the burst of infection cannot turn a localized spreading into a delocalized spreading. Our result is evaluated both on synthetic and real networks.

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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