Literature DB >> 22463288

Absence of influential spreaders in rumor dynamics.

Javier Borge-Holthoefer1, Yamir Moreno.   

Abstract

Recent research [Kitsak, Gallos, Havlin, Liljeros, Muchnik, Stanley, and Makse, Nature Physics 6, 888 (2010)] has suggested that coreness, and not degree, constitutes a better topological descriptor to identify influential spreaders in complex networks. This hypothesis has been verified in the context of disease spreading. Here, we instead focus on rumor spreading models, which are more suited for social contagion and information propagation. To this end, we perform extensive computer simulations on top of several real-world networks and find opposite results. Namely, we show that the spreading capabilities of the nodes do not depend on their k-core index, which instead determines whether or not a given node prevents the diffusion of a rumor to a system-wide scale. Our findings are relevant both for sociological studies of contagious dynamics and for the design of efficient commercial viral processes.

Mesh:

Year:  2012        PMID: 22463288     DOI: 10.1103/PhysRevE.85.026116

Source DB:  PubMed          Journal:  Phys Rev E Stat Nonlin Soft Matter Phys        ISSN: 1539-3755


  24 in total

1.  Local structure can identify and quantify influential global spreaders in large scale social networks.

Authors:  Yanqing Hu; Shenggong Ji; Yuliang Jin; Ling Feng; H Eugene Stanley; Shlomo Havlin
Journal:  Proc Natl Acad Sci U S A       Date:  2018-07-03       Impact factor: 11.205

2.  Optimal deployment of resources for maximizing impact in spreading processes.

Authors:  Andrey Y Lokhov; David Saad
Journal:  Proc Natl Acad Sci U S A       Date:  2017-09-12       Impact factor: 11.205

3.  A measure of individual role in collective dynamics.

Authors:  Konstantin Klemm; M Ángeles Serrano; Víctor M Eguíluz; Maxi San Miguel
Journal:  Sci Rep       Date:  2012-02-29       Impact factor: 4.379

4.  A process of rumour scotching on finite populations.

Authors:  Guilherme Ferraz de Arruda; Elcio Lebensztayn; Francisco A Rodrigues; Pablo Martín Rodríguez
Journal:  R Soc Open Sci       Date:  2015-09-16       Impact factor: 2.963

5.  Improving the accuracy of the k-shell method by removing redundant links: From a perspective of spreading dynamics.

Authors:  Ying Liu; Ming Tang; Tao Zhou; Younghae Do
Journal:  Sci Rep       Date:  2015-08-17       Impact factor: 4.379

6.  Detecting the influence of spreading in social networks with excitable sensor networks.

Authors:  Sen Pei; Shaoting Tang; Zhiming Zheng
Journal:  PLoS One       Date:  2015-05-07       Impact factor: 3.240

7.  Searching for superspreaders of information in real-world social media.

Authors:  Sen Pei; Lev Muchnik; José S Andrade; Zhiming Zheng; Hernán A Makse
Journal:  Sci Rep       Date:  2014-07-03       Impact factor: 4.379

8.  Locating influential nodes via dynamics-sensitive centrality.

Authors:  Jian-Guo Liu; Jian-Hong Lin; Qiang Guo; Tao Zhou
Journal:  Sci Rep       Date:  2016-02-24       Impact factor: 4.379

9.  Finding near-optimal groups of epidemic spreaders in a complex network.

Authors:  Geoffrey Moores; Paulo Shakarian; Brian Macdonald; Nicholas Howard
Journal:  PLoS One       Date:  2014-04-02       Impact factor: 3.240

10.  Measuring and modeling behavioral decision dynamics in collective evacuation.

Authors:  Jean M Carlson; David L Alderson; Sean P Stromberg; Danielle S Bassett; Emily M Craparo; Francisco Guiterrez-Villarreal; Thomas Otani
Journal:  PLoS One       Date:  2014-02-10       Impact factor: 3.240

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