Spreading Processes with Population Heterogeneity Over Multi-Layer Networks
Spreading Processes with Population Heterogeneity Over Multi-Layer Networks
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DOI：
10.1109/globecom54140.2023.10437832
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发表时间：
2022-11
期刊：
影响因子：
--
通讯作者：
Yurun Tian;Osman Yağan
中科院分区：
文献类型：
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作者：
Yurun Tian;Osman Yağan
对复杂网络上的传播过程进行建模已受到越来越多的关注。例如，考虑人口异质性的债券渗透模型已被用来深入了解疾病传播和错误信息控制。然而，大多数关于具有群体异质性的传播过程的研究仅集中在单层接触网络上。为了研究传播过程的过程如何因多层接触网络（例如，社区与学校或 Twitter 与 Facebook）而变化，同时从原则性的数学角度考虑人口异质性，我们提出了基于 SIR 动力学的多层掩模模型。我们推导出三个基本流行病学量的解析表达式：出现概率、流行阈值和预期流行规模。分析结果与通过大量模拟获得的数值结果几乎完全一致。这些结果揭示了多层接触网络的结构、病毒传播动力学和群体异质性对传播过程最终状态的影响。因此，它们可能有助于制定疾病传播和信息传播的缓解和控制策略。
Modeling spreading processes over complex networks has been receiving increasing attention. For example, bond percolation models considering population heterogeneity have been used to derive insights into disease spread and misinformation control. However, most works on spreading processes with population heterogeneity only concentrate on single-layer contact networks. To study how the course of a spreading process changes due to multiple layers of contact networks (e.g., neighborhood vs. schools or Twitter vs. Facebook) while considering population heterogeneity from a principled, mathematical lens, we propose the Multi-layer Mask model based on SIR dynamics. We derive analytical expressions for three fundamental epidemiological quantities: the probability of emergence, the epidemic threshold, and the expected epidemic size. Analytical results are shown to be in near-perfect agreement with the numerical results obtained through extensive simulations. These results reveal the impact of the structure of the multi-layer contact network, viral transmission dynamics, and population heterogeneity on the final state of the spreading process. Thus, they might help develop mitigation and control strategies for disease spread and information diffusion.