Literature DB >> 32271857

A trust model for spreading gossip in social networks: a multi-type bootstrap percolation model.

Rinni Bhansali1, Laura P Schaposnik2,3.   

Abstract

We introduce here a multi-type bootstrap percolation model, which we call T -Bootstrap Percolation ( T -BP), and apply it to study information propagation in social networks. In this model, a social network is represented by a graph G whose vertices have different labels corresponding to the type of role the person plays in the network (e.g. a student, an educator etc.). Once an initial set of vertices of G is randomly selected to be carrying a gossip (e.g. to be infected), the gossip propagates to a new vertex provided it is transmitted by a minimum threshold of vertices with different labels. By considering random graphs, which have been shown to closely represent social networks, we study different properties of the T -BP model through numerical simulations, and describe its implications when applied to rumour spread, fake news and marketing strategies.
© 2020 The Author(s).

Entities:  

Keywords:  bootstrap percolation; disease propagation; information propagation; trust model

Year:  2020        PMID: 32271857      PMCID: PMC7125993          DOI: 10.1098/rspa.2019.0826

Source DB:  PubMed          Journal:  Proc Math Phys Eng Sci        ISSN: 1364-5021            Impact factor:   2.704


  6 in total

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3.  Spreading gossip in social networks.

Authors:  Pedro G Lind; Luciano R da Silva; José S Andrade; Hans J Herrmann
Journal:  Phys Rev E Stat Nonlin Soft Matter Phys       Date:  2007-09-27

4.  Heterogeneous k-core versus bootstrap percolation on complex networks.

Authors:  G J Baxter; S N Dorogovtsev; A V Goltsev; J F F Mendes
Journal:  Phys Rev E Stat Nonlin Soft Matter Phys       Date:  2011-05-31

5.  Insights into bootstrap percolation: Its equivalence with k-core percolation and the giant component.

Authors:  Matías A Di Muro; Lucas D Valdez; H Eugene Stanley; Sergey V Buldyrev; Lidia A Braunstein
Journal:  Phys Rev E       Date:  2019-02       Impact factor: 2.529

6.  Understanding the temporal pattern of spreading in heterogeneous networks: Theory of the mean infection time.

Authors:  Mi Jin Lee; Deok-Sun Lee
Journal:  Phys Rev E       Date:  2019-03       Impact factor: 2.529

  6 in total
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Authors:  Anna Kłak; Jolanta Grygielska; Małgorzata Mańczak; Ewelina Ejchman-Pac; Jakub Owoc; Urszula Religioni; Robert Olszewski
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