2016/12/05 by Piet Van Mieghem, Van Mieghem, Piet
Biochemistry, Genetics and Molecular Biology · Computer Science · Physics and Astronomy · Psychology · #Complex Network Analysis Techniques #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Physical sciences #Mental Health Research Topics #Opinion Dynamics and Social Influence #Physics and Society (physics.soc-ph) #Populations and Evolution (q-bio.PE) #Social and Information Networks (cs.SI) #cs.SI #physics.soc-ph #q-bio.PE
paper · pdf · doi:10.48550/arxiv.1612.01386
arxiv created 2016/12/05 · openalex publication_date 2016/12/05 · arxiv updated 2016/12/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Epidemic models are increasingly used in real-world networks to understand diffusion phenomena (such as the spread of diseases, emotions, innovations, failures) or the transport of information (such as news, memes in social on-line networks). A new analysis of the prevalence, the expected number of infected nodes in a network, is presented and physically interpreted. The analysis method is based on spectral decomposition and leads to a universal, analytic curve, that can bound the time-varying prevalence in any finite time interval. Moreover, that universal curve also applies to various types of Susceptible-Infected-Susceptible (SIS) (and Susceptible-Infected-Removed (SIR)) infection processes, with both homogenous and heterogeneous infection characteristics (curing and infection rates), in temporal and even disconnected graphs and in SIS processes with and without self-infections. The accuracy of the universal curve is comparable to that of well-established mean-field approximations.