2017/07/07 by Samuel Unicomb, Gerardo Iñiguez, Gerardo Íñiguez +1 · 3 citations
Computer Science · Mathematics · Physics and Astronomy · Psychology · #Artificial intelligence #Binary number #Cascade #Complex Network Analysis Techniques #Complex network #Complex system #Computer science #Dynamics (music) #Enhanced Data Rates for GSM Evolution #Information cascade #Machine learning #Mathematics #Mental Health Research Topics #Network structure #Node (physics) #Opinion Dynamics and Social Influence #Physics #Statistical physics #Statistics #Theoretical computer science #Threshold model #cond-mat.stat-mech #cs.SI #physics.data-an #physics.soc-ph
paper · pdf · doi:10.1038/s41598-018-21261-9
published as Sci. Rep. 8, 3094 (2018) · 24 pages, 11 figures
arxiv created 2017/07/07 · openalex publication_date 2018/02/09 · arxiv updated 2021/04/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
Weighted networks capture the structure of complex systems where interaction strength is meaningful. This information is essential to a large number of processes, such as threshold dynamics, where link weights reflect the amount of influence that neighbours have in determining a node's behaviour. Despite describing numerous cascading phenomena, such as neural firing or social contagion, the modelling of threshold dynamics on weighted networks has been largely overlooked. We fill this gap by studying a dynamical threshold model over synthetic and real weighted networks with numerical and analytical tools. We show that the time of cascade emergence depends non-monotonously on weight heterogeneities, which accelerate or decelerate the dynamics, and lead to non-trivial parameter spaces for various networks and weight distributions. Our methodology applies to arbitrary binary state processes and link properties, and may prove instrumental in understanding the role of edge heterogeneities in various natural and social phenomena.