Coupled spreading between information and epidemics on multiplex networks with simplicial complexes

Junfeng Fan, Dawei Zhao, Chengyi Xia, Jun Tanimoto

研究成果: ジャーナルへの寄稿学術誌査読

13 被引用数 (Scopus)


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.

出版ステータス出版済み - 11月 1 2022

!!!All Science Journal Classification (ASJC) codes

  • 統計物理学および非線形物理学
  • 数理物理学
  • 物理学および天文学一般
  • 応用数学


「Coupled spreading between information and epidemics on multiplex networks with simplicial complexes」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。