URU-SIS: A model for information propagation in multiplex networks

Considering that users exhibit different preferences for various social media platforms, which influence their behaviors and consequently the likelihood of spreading information, we establish the URU-SIS (uninformed–received–uninformed, susceptible–infected–susceptible) model to investigate information diffusion in multiplex social networks with heterogeneous transmission probabilities. Specifically, the model employs a two-layer coupling mechanism, consisting of a main network (SIS) and an auxiliary network (URU), in which information spreading in the auxiliary network reinforces the effective spreading rate in the main network. Based on the Microscopic Markov Chain Method, we construct propagation trees to describe the state transitions of nodes and formulate the corresponding dynamic equations. Furthermore, we derive the propagation threshold of the model, which depends on the network topology, and we verify it through extensive simulations. Additional experiments demonstrate that information propagation in the auxiliary network promotes the propagation process in the main network, and that higher transmission probabilities lead to faster diffusion and broader coverage. Moreover, comparisons between simulated and real-world diffusion data indicate that the model effectively reproduces the trends and dynamic patterns of information propagation. Our results suggest that the URU-SIS model effectively captures the essential features of information diffusion in multiplex networks and offers valuable insights into the impact of external factors on information propagation in social networks.