Information-Epidemic Dynamics in Cyber-Physical Systems: A Hypergraph Framework with Interpersonal Relationships

2026-06-30

IEEE Internet of Things Journal

AI Summary

This study addresses the often-overlooked role of interpersonal relationship heterogeneity in the coupled dynamics of information dissemination and epidemic spreading. The authors propose a two-layer hypergraph framework, where the upper layer captures high-order information propagation and the lower layer models SIS epidemic dynamics. An adaptive awareness-protection mechanism based on Jaccard similarity is introduced to describe how individuals dynamically adjust protective behaviors according to the closeness of their social ties. For the first time, relational heterogeneity is integrated into an information-epidemic coupled system. Combining microscopic Markov chain analysis with Monte Carlo simulations, the work demonstrates that strong interpersonal relationships not only enhance information diffusion but also significantly raise the epidemic threshold and suppress outbreak size, offering theoretical foundations for targeted intervention strategies.

Abstract

Understanding how information propagation affects epidemic dynamics has become an emerging topic of interest. However, the influence of interpersonal relationship heterogeneity on information acquisition and disease transmission has been largely overlooked. In this work, we introduce a hypergraph structure for Cyber-Physical Systems (CPSs) with two distinct layers. The upper layer, referred to as the cyber layer, consists of a mixed hypergraph, capturing both pairwise propagation and higher-order diffusion of epidemic-related information. The lower layer, referred to as the physical layer, employs a Susceptible-Infected-Susceptible (SIS) process to capture epidemic spreading. This work introduces an adaptive perception-protection mechanism based on Jaccard similarity, which accounts for interpersonal heterogeneity. In this mechanism, individuals receive information based on their relationships with neighbors and take protective measures accordingly. We analyze the impact of interpersonal relationships and the adoption of neighborhood-based self-protection strategies on epidemic dynamics. Furthermore, we conduct a theoretical analysis based on the Microscopic Markov Chain Approach (MMCA), analytically derive the outbreak threshold, and confirm the results with extensive Monte Carlo (MC) simulations. The results show that stronger interpersonal relationships can promote information propagation, significantly increase the threshold for epidemic outbreaks, and effectively suppress the scale of the epidemic. The study provides theoretical support for designing epidemic control strategies considering interpersonal heterogeneity and improves the understanding of epidemic spreading on hypergraphs.

Problem

• information-epidemic dynamics
• interpersonal relationships
• hypergraph
• Cyber-Physical Systems
• epidemic spreading

Innovation

• hypergraph
• information-epidemic coupling
• interpersonal heterogeneity
• adaptive perception-protection mechanism
• Microscopic Markov Chain Approach