Novel propagation phenomena: behaviors of local trend imitation on multiple limited contact networks

Abstract

The local trend imitation(LTI) feature behavior has been deeply studied on specific complex networks, but still needs to be explored in more scenarios. In fact, the multiple networks with individual limited contact feature is more in line with the real scenario On the multiple limited networks,a novel model is proposed to investigate the effects of individual contact capacity heterogeneity. Then,information propagation mechanism is then measured and examined using a developed partition theory. The experimental results show crossover occurrences of phase transition. In the new network model, the final spreading scope exhibits a second-order continuous growth when individuals display a positive LTI behavior. Individuals have a passive LTI behavior, however, the final spreading scope exhibits a first-order discontinuous growth. Besides, a greatest ultimate spreading size appears at an ideal LTI parameter with the unit spreading probability changing. Additionally, individual contact capacity heterogeneity changes the rate of information spreading and the global adoption pattern. Eventually the outcomes of the theoretic analysis match those of the simulations.

Study on the influence of the information diffusion and vaccination behavior on disease transmission in multiplex networks

“…In fact, when disease outbreak occurs, disease-related information also diffuses among the population, and we learn about the risk of the disease and some preventive measures based on this information as a way to reduce our risk of being infected, which in turn has an important impact on the transmission of the disease [20][21][22][23][24][25]. Funk et al [22] analyzed the law of disease transmission based on the SIR model, and found that the diffusion of information can reduce the scale of the transmission of the disease; Granell et al [23] established UAU-SIS twolayer network model to study the coupled information and disease spreading law, and found that information can effectively inhibit disease transmission at the meta-critical point.…”