2019/10/31 by Hao Peng, Azadeh Nematzadeh, Daniel M. Romero +1 · 27 citations
Computer Science · Engineering · Mathematics · Physics and Astronomy · Social Sciences · #Artificial intelligence #Cascade #Complex Network Analysis Techniques #Complex network #Complex system #Computer science #Data science #Diffusion #Diffusion process #Distributed computing #Engineering #Evolutionary Game Theory and Cooperation #Feature (linguistics) #Information cascade #Information flow #Innovation diffusion #Knowledge management #Mathematics #Modular design #Modularity (biology) #Network topology #Opinion Dynamics and Social Influence #Physics #Process (computing) #Social media #Theoretical computer science #Topology (electrical circuits) #Viral marketing #World Wide Web #cs.SI #physics.soc-ph
paper · pdf · doi:10.1103/physreve.102.052316
published in Physical review. E 102(5), 052316 (American Physical Society)
openalex created_date 2019/10/18 · arxiv created 2020/07/30 · openalex publication_date 2020/11/30 · arxiv updated 2020/12/30 · openalex updated_date 2026/08/05
The rapid diffusion of information and the adoption of social behaviors are of critical importance in situations as diverse as collective actions, pandemic prevention, or advertising and marketing. Although the dynamics of large cascades have been extensively studied in various contexts, few have systematically examined the impact of network topology on the efficiency of information diffusion. Here, by employing the linear threshold model on networks with communities, we demonstrate that a prominent network feature-the modular structure-strongly affects the speed of information diffusion in complex contagion. Our simulations show that there always exists an optimal network modularity for the most efficient spreading process. Beyond this critical value, either a stronger or a weaker modular structure actually hinders the diffusion speed. These results are confirmed by an analytical approximation. We further demonstrate that the optimal modularity varies with both the seed size and the target cascade size and is ultimately dependent on the network under investigation. We underscore the importance of our findings in applications from marketing to epidemiology, from neuroscience to engineering, where the understanding of the structural design of complex systems focuses on the efficiency of information propagation.