Zhuang Yan, Li Weihua, Liu Yang
School of Economics and Management, Beihang University, Beijing 100191, China.
School of Software, Beihang University, Beijing 100191, China.
Entropy (Basel). 2025 Feb 24;27(3):234. doi: 10.3390/e27030234.
Information and knowledge diffusion are important dynamical processes in complex social systems, in which the underlying topology of interactions among individuals is often modeled as networks. Recent studies have examined various information diffusion scenarios primarily focusing on the dynamics within one network; yet, relatively little scholarly attention has been paid to possible interactions among individuals beyond the focal network. Here, in this study, we account for this phenomenon by modeling the information diffusion dynamics with the involvement of independent spreaders in a susceptible-exposed-infectious-recovered contagion process. Independent spreaders receive information using latent information transmission pathways without following the links in the focal network and can spread the information to remote areas of the network not well connected to the major components. We derive the mathematics of the critical epidemic thresholds on homogeneous and heterogeneous networks as a function of the infectious rate, exposure rate, recovery rate and the activeness of independent spreaders. We present simulation results on Small World and Scale-Free complex networks, and real-world social networks of Facebook artists and physicist collaborations. The result shows that the extent to which information or knowledge can spread might be more extensive than we can explain in terms of link contagion only. In addition, these results also help to explain how the activeness of independent spreaders can affect the diffusion process of information and knowledge in complex networks, which may have implications for studies exploring other dynamical processes.
信息和知识传播是复杂社会系统中的重要动态过程，其中个体间潜在的互动拓扑结构通常被建模为网络。最近的研究主要聚焦于一个网络内部的动态过程，考察了各种信息传播场景；然而，相对较少有学术关注焦点网络之外个体间可能存在的互动。在此研究中，我们通过在易感-暴露-感染-康复的传染过程中纳入独立传播者来对信息传播动态进行建模，以此来解释这一现象。独立传播者利用潜在信息传播路径获取信息，而不遵循焦点网络中的链接，并且能够将信息传播到与主要组件连接不佳的网络偏远区域。我们推导出均匀网络和异质网络上临界流行阈值的数学表达式，该表达式是传染率、暴露率、康复率以及独立传播者活跃度的函数。我们展示了在小世界网络和无标度复杂网络以及Facebook上艺术家和物理学家合作的真实社会网络上的模拟结果。结果表明，信息或知识能够传播的范围可能比仅从链接传染角度所能解释的更为广泛。此外，这些结果也有助于解释独立传播者的活跃度如何影响复杂网络中信息和知识的传播过程，这可能对探索其他动态过程的研究具有启示意义。
