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Phase transitions in information spreading on structured populations

Phase transitions in information spreading on structured populations

Davis, Jessica T., Perra, Nicola, Zhang, Qian, Moreno, Yamir and Vespignani, Alessandro (2020) Phase transitions in information spreading on structured populations. Nature Physics, 16 (5). pp. 590-596. ISSN 1745-2473 (Print), 1745-2481 (Online) (doi:https://doi.org/10.1038/s41567-020-0810-3)

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Abstract

Mathematical models of social contagion that incorporate networks of human interactions have become increasingly popular, however, very few approaches have tackled the challenges of including complex and realistic properties of socio-technical systems. In this work we define a framework to characterize the dynamics of the Maki-Thompson rumor spreading model in structured populations, and analytically find a previously uncharacterized dynamical phase transition that separates the local and global contagion regimes. We validate our threshold prediction through extensive Monte Carlo simulations. Furthermore, we apply this framework in two real-world systems, the European commuting and transportation network and the Digital Bibliography and Library Project (DBLP) collaboration network. Our findings highlight the importance of the underlying population structure in understanding social contagion phenomena and have the potential to define new intervention strategies aimed at hindering or facilitating the diffusion of information in socio-technical systems.

Item Type: Article
Uncontrolled Keywords: networks science, processes on networks, contagion processes
Subjects: H Social Sciences > H Social Sciences (General)
Faculty / Department / Research Group: Faculty of Business
Faculty of Business > Department of International Business & Economics
Faculty of Business > Networks and Urban Systems Centre (NUSC)
Faculty of Business > Networks and Urban Systems Centre (NUSC) > Centre for Business Network Analysis (CBNA)
Last Modified: 14 Jul 2020 15:33
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
Selected for REF2021: REF 2
URI: http://gala.gre.ac.uk/id/eprint/26733

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