Modeling the interactive diffusion of information in multilayer networks with simplicial complexes

The diffusion of information in social networks is jointly shaped by network topology and the interaction among multiple types of information. Structurally, social networks contain not only pairwise relationships but also numerous group-based interactions. Dynamically, different information types may facilitate or inhibit each other during diffusion. However, most existing models rely on low-order networks built from pairwise links, limiting their ability to capture higher-order effects arising from group interactions. To address this limitation, this study proposes a novel information diffusion model that integrates simplicial complexes and multilayer coupling mechanisms, enabling the coexistence of individual- and group-level interactions as well as the mutual influence among different information types. The model extends the classical unaware–aware–unaware (UAU) paradigm and considers four node states to represent the interactive diffusion of two types of information. Using the Microscopic Markov Chain Approach (MMCA), we derive a set of dynamic evolution equations and analyze the critical diffusion threshold via mean-field theory. Simulation results verify the accuracy of the theoretical analysis and demonstrate that higher-order structures obviously reduce the threshold and enhance diffusion performance. Moreover, the model reveals how mutual facilitation or inhibition between information types and the density of higher-order structures jointly affect the scale and speed of diffusion. This work provides new insights into the co-evolutionary dynamics of information and has implications for designing effective information propagation strategies in real-world complex networks.