Li Weihua, Tang Shaoting, Fang Wenyi, Guo Quantong, Zhang Xiao, Zheng Zhiming
School of Mathematics and Systems Science, Beihang University, Beijing 100191, China.
Key Laboratory of Mathematics Informatics Behavioral Semantics (LMIB), Ministry of Education, China.
Phys Rev E Stat Nonlin Soft Matter Phys. 2015 Oct;92(4):042810. doi: 10.1103/PhysRevE.92.042810. Epub 2015 Oct 20.
The information diffusion process in single complex networks has been extensively studied, especially for modeling the spreading activities in online social networks. However, individuals usually use multiple social networks at the same time, and can share the information they have learned from one social network to another. This phenomenon gives rise to a new diffusion process on multiplex networks with more than one network layer. In this paper we account for this multiplex network spreading by proposing a model of information diffusion in two-layer multiplex networks. We develop a theoretical framework using bond percolation and cascading failure to describe the intralayer and interlayer diffusion. This allows us to obtain analytical solutions for the fraction of informed individuals as a function of transmissibility T and the interlayer transmission rate θ. Simulation results show that interaction between layers can greatly enhance the information diffusion process. And explosive diffusion can occur even if the transmissibility of the focal layer is under the critical threshold, due to interlayer transmission.
单一复杂网络中的信息扩散过程已得到广泛研究，特别是用于对在线社交网络中的传播活动进行建模。然而，个体通常会同时使用多个社交网络，并且能够将他们从一个社交网络学到的信息分享到另一个社交网络。这种现象引发了在具有多个网络层的多重网络上的一种新的扩散过程。在本文中，我们通过提出一个两层多重网络中的信息扩散模型来解释这种多重网络传播。我们利用键渗流和级联故障开发了一个理论框架来描述层内和层间扩散。这使我们能够获得作为传播率T和层间传输率θ的函数的知情个体比例的解析解。仿真结果表明，层间相互作用可以极大地增强信息扩散过程。并且由于层间传输，即使焦点层的传播率低于临界阈值，也可能发生爆发性扩散。
