SELHR: A Novel Epidemic-Based Model for Information Propagation in Complex Networks

Abstract

The study of information spreading based on the complex network theory and topological structure has become an important issue in complex networks. Plenty of infectious disease models are widely used for information diffusion research in complex networks. Based on these state-of-the-art models, a new epidemic dynamic model with dynamic evolution equations is proposed and performed on the homogeneous and heterogeneous networks, respectively, in this paper. Meanwhile, we divide the propagation states into two states: L and H (low propagation ability groups and high propagation ability groups) and consider the transformation of these two states in our model. Then, the equilibria and stability of the model are analyzed for both homogeneous and heterogeneous networks to verify the validity of the proposed model. Finally, simulation results illustrate that the proposed model and information propagation dynamic evolution equations are reasonable and effective. Experiments with effect factors also reveal the interaction mechanism and the diffusion process of the proposed model in complex networks.

Modeling Information Warfare in Scale-Free Networks: Analysis of Propagation and Counter-Propagation Dynamics

“…Numerous evolving information diffusion models exist within literature, targeting the spread of information in complex networks. [17] introduced an epidemic SIR-based dynamic model, distinguishing propagation states into two categories: L (low propagation ability groups) and H (high propagation ability groups). Their work contributes by capturing the heterogeneous propagation ability of nodes, analyzing the network structure's impact, and simulating information diffusion in both homogeneous and heterogeneous networks.…”