Information dissemination in growing scale-free hypernetworks with tunable clustering

Most real-world network evolution mechanisms not only have a preference attachment mechanism, but also exhibit high clustering characteristics. The existing information dissemination hypernetwork models are based on scale-free hypernetworks, and in this paper, we extend the scale-free hypernetwork evolution model by adding an adjustable high clustering and growth mechanism based on preference attachment, and propose a growing scale-free hypernetwork with tunable clustering. Thus hypernetwork models extend the traditional models and are more realistic. An information propagation model of SIS in hypernetworks based on reaction process strategy is constructed, and the dynamic process of information propagation under different network structure parameters is theoretically analyzed and numerically simulated. The results show that the propagation capacity of information increase with the growth rate, but suppressed with the increase of clustering coefficient. Additionally, we have discovered an important phenomenon: when the growth rate reaches 0.4 and increases further, the density of information nodes reaches saturation in the steady state. The proposed hypernetwork model is more suitable for real social networks and can provide some theoretical references for public opinion prediction and information control.