微信群与线下交流耦合网络知识传播模型研究

本文构建了微信群与线下交流耦合网络上的知识传播模型,该模型考虑了微信群中知识交流次数的变化对线下子网络中知识传播率的影响.推导出区分知识在耦合网络中传播与否的阈值条件,并验证了传播阈值始终是一有限数,最后结合实际数据对耦合网络上的知识传播过程进行了数值模拟.结果表明,耦合网络中线下子网络层的传播阈值大于等于单一的线下网络的传播阈值,而小于等于耦合网络的传播阈值.与各层子网络传播率均为正常数的耦合网络模型相比,在线下传播率随微信群中知识交流次数变化的模型中知识的传播阈值和最终传播规模更大,研究还表明,网络结构对知识传播有重要影响,相比于均匀网络,如果线下子网络是无标度的,即使初始时刻知识拥有者数量较少,耦合网络中知识的传播阈值与最终规模也会更大,传播速度更快.

Knowledge transmission model on the coupled network formed by WeChat group and offline communication

This paper constructs a knowledge transmission model on the coupled network formed by WeChat group and offline communication. The model considers the effect of the change in the number of exchange of knowledge in the WeChat group on the knowledge spreading rate in the offline subnetwork. The threshold conditions for distinguishing whether the knowledge propagates in the coupled network are derived. Further, it is verified that the transmission threshold is always a finite number. Finally, the numerical simulations of the propagation process on the coupled network are conducted based on the actual data. Results show that the transmission threshold in the offline subnet layer of the coupled network is greater than or equal to the transmission threshold in a single offline network, and less than or equal to the transmission threshold in the coupled network. The transmission threshold and the final propagation scale are larger in the model of the online communication rate changing with the number of exchange of the knowledge in the WeChat group compared with the coupled network model with a positive constant transmission rate in one layer. Study also shows that the network structure has a significant impact on knowledge transmission. If the offline sub-network is scale-free, then the transmission threshold and the final size of knowledge spreading in the coupled network will be larger compared to that is homogeneous network even if the number of knowledge owners at the initial time is small. If the offline sub-network is scale-free, then the spreading speed is faster compared to that is homogeneous network.