Modeling the information of social contagion processes has recently attracted a substantial amount of interest from researchers due to its wide applicability in network science, multi-agent-systems, information science, and marketing. Unlike in biological spreading, the existence of a reinforcement effect in social contagion necessitates considering the complexity of individuals in the systems. Although many studies acknowledged the heterogeneity of the individuals in their adoption of information, there are no studies that take into account the individuals’ uncertainty during their adoption decision-making. This resulted in less than optimal modeling of social contagion dynamics in the existence of phase transition in the final adoption size versus transmission probability. We employed the Inverse Born Problem (IBP) to represent probabilistic entities as complex probability amplitudes in edge-based compartmental theory, and demonstrated that our novel approach performs better in the prediction of social contagion dynamics through extensive simulations on random regular networks.

中文翻译：

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量子传染：一种采用异类采用阈值的社会传染动力学的类似量子方法

由于社会传播过程的信息建模在网络科学，多代理系统，信息科学和市场营销中的广泛应用，最近引起了研究人员的极大兴趣。与生物传播不同，社会传播中存在增强效应，因此有必要考虑系统中个体的复杂性。尽管许多研究承认个人在采用信息方面的异质性，但没有研究考虑到个人在采用决策过程中的不确定性。在最终采用规模与传播概率之间存在相变的情况下，这导致了社会传染动力学的最优建模。