The impact of community heterogeneity on information propagation and virus transmission in multi-layer networks

Abstract

Community structures are ubiquitous in real-world networks, and community heterogeneity plays a pivotal role in the coupled dynamics of information propagation and virus transmission. Moreover, higher-order structures, such as 2-simplex, offer a novel theoretical framework for studying multi-node interactions. To explore these dynamics, a two-layer heterogeneous community network model is developed, where the upper layer represents information propagation dynamics, and the lower layer captures virus transmission processes. Through simulations, we investigate the impact of community heterogeneity and higher-order structures on propagation speed, threshold, and final scale. Results demonstrate that community heterogeneity significantly regulates the dynamics of information and virus propagation. We consider two scenarios where community heterogeneity exists in the upper and lower layers of the network. In both cases, networks with highly heterogeneous communities have a lower threshold than those with homogeneous communities. When community heterogeneity is present in the upper layer, the propagation scale of the highly heterogeneous community network is larger than that of the homogeneous network under the same virus transmission rate. In the case of heterogeneity in the lower layer, for lower virus transmission rates, the propagation scale of the highly heterogeneous community network is larger than that of the homogeneous network, while for higher virus transmission rates, the propagation scale of the highly heterogeneous community network is smaller than that of the homogeneous network. Additionally, higher-order structures play a crucial role in enhancing information propagation efficiency and suppressing virus transmission. This study provides new theoretical insights into propagation dynamics in complex networks and offers a foundation for optimizing public health interventions.