Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin University of Technology, Tianjin 300384, China.
Shandong Provincial Key Laboratory of Computer Networks, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China.
Chaos. 2023 Jun 1;33(6). doi: 10.1063/5.0151881.
Over the past decade, the coupled spread of information and epidemic on multiplex networks has become an active and interesting topic. Recently, it has been shown that stationary and pairwise interactions have limitations in describing inter-individual interactions , and thus, the introduction of higher-order representation is significant. To this end, we present a new two-layer activity-driven network epidemic model, which considers the partial mapping relationship among nodes across two layers and simultaneously introduces simplicial complexes into one layer, to investigate the effect of 2-simplex and inter-layer mapping rate on epidemic transmission. In this model, the top network, called the virtual information layer, characterizes information dissemination in online social networks, where information can be diffused through simplicial complexes and/or pairwise interactions. The bottom network, named as the physical contact layer, denotes the spread of infectious diseases in real-world social networks. It is noteworthy that the correspondence among nodes between two networks is not one-to-one but partial mapping. Then, a theoretical analysis using the microscopic Markov chain (MMC) method is performed to obtain the outbreak threshold of epidemics, and extensive Monte Carlo (MC) simulations are also carried out to validate the theoretical predictions. It is obviously shown that MMC method can be used to estimate the epidemic threshold; meanwhile, the inclusion of simplicial complexes in the virtual layer or introductory partial mapping relationship between layers can inhibit the spread of epidemics. Current results are conducive to understanding the coupling behaviors between epidemics and disease-related information.
在过去的十年中，信息和传染病在多重网络上的耦合传播已经成为一个活跃而有趣的研究课题。最近的研究表明，固定的和成对的相互作用在描述个体间相互作用方面存在局限性，因此引入高阶表示是非常重要的。为此，我们提出了一种新的双层活动驱动网络传染病模型，该模型考虑了两层节点之间的部分映射关系，并在其中一层引入单纯形复形，以研究二阶单纯形和层间映射率对传染病传播的影响。在这个模型中，顶层网络被称为虚拟信息层，它描述了在线社交网络中的信息传播，其中信息可以通过单纯形复形和/或成对相互作用进行扩散。底层网络被称为物理接触层，它表示现实社交网络中传染病的传播。值得注意的是，两个网络中节点之间的对应关系不是一一对应的，而是部分映射的。然后，我们使用微观马尔可夫链（MMC）方法进行理论分析，以获得传染病的爆发阈值，并进行了广泛的蒙特卡罗（MC）模拟来验证理论预测。显然，MMC 方法可以用来估计传染病的阈值；同时，在虚拟层中包含单纯形复形或层间的部分映射关系可以抑制传染病的传播。目前的研究结果有助于理解传染病与疾病相关信息之间的耦合行为。
