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. 2022 Nov;32(11):113115. doi: 10.1063/5.0125873.
The way of information diffusion among individuals can be quite complicated, and it is not only limited to one type of communication, but also impacted by multiple channels. Meanwhile, it is easier for an agent to accept an idea once the proportion of their friends who take it goes beyond a specific threshold. Furthermore, in social networks, some higher-order structures, such as simplicial complexes and hypergraph, can describe more abundant and realistic phenomena. Therefore, based on the classical multiplex network model coupling the infectious disease with its relevant information, we propose a novel epidemic model, in which the lower layer represents the physical contact network depicting the epidemic dissemination, while the upper layer stands for the online social network picturing the diffusion of information. In particular, the upper layer is generated by random simplicial complexes, among which the herd-like threshold model is adopted to characterize the information diffusion, and the unaware-aware-unaware model is also considered simultaneously. Using the microscopic Markov chain approach, we analyze the epidemic threshold of the proposed epidemic model and further check the results with numerous Monte Carlo simulations. It is discovered that the threshold model based on the random simplicial complexes network may still cause abrupt transitions on the epidemic threshold. It is also found that simplicial complexes may greatly influence the epidemic size at a steady state.
个体之间的信息传播方式可能非常复杂，不仅限于一种类型的交流，还会受到多种渠道的影响。此外，一旦朋友中接受某个观点的比例超过特定阈值，代理人就更容易接受这个观点。此外，在社交网络中，一些高阶结构，如单纯复形和超图，可以描述更丰富和现实的现象。因此，基于经典的传染病与其相关信息的多重网络模型，我们提出了一种新的传染病模型，其中底层表示描述传染病传播的物理接触网络，而上层表示描述信息扩散的在线社交网络。特别地，上层网络是通过随机单纯复形生成的，其中采用了羊群阈值模型来描述信息扩散，同时也考虑了无意识-意识-无意识模型。我们使用微观马尔可夫链方法分析了所提出的传染病模型的传染病阈值，并通过大量的蒙特卡罗模拟进行了验证。结果表明，基于随机单纯复形网络的阈值模型可能仍然会导致传染病阈值的突然转变。此外，还发现单纯复形可能会极大地影响传染病在稳定状态下的规模。
