2015/08/31 by Hyewon Kim, Meesoon Ha, Hawoong Jeong
Computer Science · Mathematics · Physics and Astronomy · #COVID-19 epidemiological studies #Complex Network Analysis Techniques #Context (archaeology) #Focus (optics) #Network structure #Opinion Dynamics and Social Influence #Property (philosophy) #Relation (database) #Scaling #Universality (dynamical systems) #cond-mat.dis-nn #cs.SI #physics.soc-ph
paper · pdf · doi:10.1140/epjb/e2015-60662-7
published as Eur. Phys. J. B (2015) 88: 315 · 8 pages, 10 figures (published version)
openalex publication_date 2015/11/28 · arxiv created 2015/12/14 · arxiv updated 2015/12/15 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/05
The formation of network structure is mainly influenced by an individual node's activity and its memory, where activity can usually be interpreted as the individual inherent property and memory can be represented by the interaction strength between nodes. In our study, we define the activity through the appearance pattern in the time-aggregated network representation, and quantify the memory through the contact pattern of empirical temporal networks. To address the role of activity and memory in epidemics on time-varying networks, we propose temporal-pattern coarsening of activity-driven growing networks with memory. In particular, we focus on the relation between time-scale coarsening and spreading dynamics in the context of dynamic scaling and finite-size scaling. Finally, we discuss the universality issue of spreading dynamics on time-varying networks for various memory-causality tests.