Information propagation influenced by individual fashion-passion trend on multi-layer weighted network

In recent years, prior research on information propagation has considered individual relationships on social network as binary but ignored individual intimacy heterogeneity. Furthermore, individuals like to concurrently use multiple social networks and meanwhile show different passions for information acceptance, called individual fashion-passion trend (IFPT) characteristics. Therefore, we first construct a multi-layer weighted social network to catch individual intimacy heterogeneity, and then build an adoption threshold model with tent-like probability function to explore the IFPT characteristics. Next a partition theory based on edge-weight and IFPT threshold is utilized to quantify and analyze individual information propagation mechanism. The simulated results and theoretical analyses exhibit crossover phenomena of phase transition. When individual has a strong IFPT, the increasing style of the final adoption size shows a second-order continuous phase transition. While individual has a weak IFPT, the increasing style of the final adoption size exhibits a first-order discontinuous phase transition. More excitingly, fixing the value of the information propagation unit probability, a maximum final adoption size can be obtained at an optimal IFPT value. Moreover, weight distribution heterogeneity accelerates information propagation and the change of phase transition style from the second-order continuous phase transition to first-order discontinuous phase transition. Finally, our theoretical analyses coincide with the simulated results.