Rumor propagation with heterogeneous transmission in social networks

Rumor models consider that information transmission occurs with the same probability between each pair of nodes. However, this assumption is not observed in social networks, which contain influential spreaders. To overcome this limitation, we assume that central individuals have a higher capacity to convince their neighbors than peripheral subjects. From extensive numerical simulations we find that spreading is improved in scale-free networks when the transmission probability is proportional to the PageRank, degree, and betweenness centrality. In addition, the results suggest that spreading can be controlled by adjusting the transmission probabilities of the most central nodes. Our results provide a conceptual framework for understanding the interplay between rumor propagation and heterogeneous transmission in social networks. (paper: interdisciplinary statistical mechanics)