The spreading process of information on complex networks has been widely explored. In fact, different individuals in a network usually hold different standards for information adoption. Considering the heterogeneity of adoption thresholds, this study constructs a two-layer network model with limited contacts. The adoption threshold of a node is related to its degree and a parameter obeying truncated normal distribution. This study also proposes a partition theory based on edges to analyze the mechanism of information dissemination quantitatively. Experiments find that increasing the mean of parameters can inhibit information from spreading, and the effect of the standard deviation of parameters on information dissemination depends on the mean of parameters. For instance, when the mean of parameters is a low value, as the standard deviation of parameters increases, the information outbreak size will decrease. On the other hand, the information outbreak size will increase continuously with increased propagation probability. If the mean of parameters is high, the information outbreak size will increase first and then decrease with the increment in the standard deviation of parameters. The theoretical predictions of this study are in good agreement with the numerical simulations.

信息在复杂网络上的传播过程得到了广泛的探索。事实上，网络中的不同个人通常对信息采用有不同的标准。考虑到采用阈值的异质性，本研究构建了一个具有有限联系的两层网络模型。节点的采用阈值与其程度和服从截断正态分布的参数有关。本研究还提出了一种基于边的划分理论来定量分析信息传播的机制。实验发现，增加参数的均值可以抑制信息的传播，参数标准差对信息传播的影响取决于参数的均值。例如，当参数的均值较低时，随着参数标准差的增大，信息爆发规模会减小。另一方面，信息爆发规模会随着传播概率的增加而不断增加。如果参数均值较高，则信息爆发规模将随着参数标准差的增加先增大后减小。本研究的理论预测与数值模拟非常吻合。