LAUSR

In recent years, information spreading mechanism on the social networks has received extensive attention. Previous studies usually simplify the social relationships as binary, though in real social networks, the different intimacy between nodes in different layers will affect the information spreading. The enhancement of non-redundant information memory also plays an important role in information spreading. In this paper, we propose a weighted two-layered social network information spreading model based on threshold model. In order to qualitatively understand the impact of weight distribution heterogeneity on information spreading, an edge-weight based compartmental theory is proposed. We find that under an arbitrary adoption threshold, reducing weight distribution heterogeneity can facilitate information spreading and promote the global adoption, yet unable to change the dependence pattern of the final adoption size on the unit transmission probability. We also find that when the initial seed fraction is less than a critical value, information cannot be transmitted explosively in the network. Increasing the fraction of initial seeds or the heterogeneity of degree distribution will alter the dependence pattern of the final adoption size on the unit transmission probability from being discontinuous to being continuous.