2010/06/30 by Byungjoon Min, K. -I. Goh, K.-I. Goh +2 · 112 citations
Physics and Astronomy · Psychology · Social Sciences · #Artificial intelligence #Complex Network Analysis Techniques #Computer science #Dynamics (music) #Human Mobility and Location-Based Analysis #Human dynamics #Opinion Dynamics and Social Influence #Psychology #cond-mat.stat-mech #physics.data-an #physics.soc-ph
paper · pdf · doi:10.1103/physreve.83.036102
published in Physical Review E 83(3), 036102 (American Physical Society) · 5 pages. 4 figures
openalex publication_date 2011/03/07 · arxiv created 2011/03/08 · arxiv updated 2011/03/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
We study the susceptible-infected model with power-law waiting time distributions P(τ)~τ-α, as a model of spreading dynamics under heterogeneous human activity patterns. We found that the average number of new infections n(t) at time t decays as a power law in the long-time limit, n(t)~t-β, leading to extremely slow prevalence decay. We also found that the exponent in the spreading dynamics β is related to that in the waiting time distribution α in a way depending on the interactions between agents but insensitive to the network topology. These observations are well supported by both the theoretical predictions and the long prevalence decay time in real social spreading phenomena. Our results unify individual activity patterns with macroscopic collective dynamics at the network level.