Literature DB >> 30180601

Double transition of information spreading in a two-layered network.

Jiao Wu1, Muhua Zheng2, Wei Wang3, Huijie Yang1, Changgui Gu1.   

Abstract

A great deal of significant progress has been seen in the study of information spreading on populations of networked individuals. A common point in many of the past studies is that there is only one transition in the phase diagram of the final accepted size versus the transmission probability. However, whether other factors alter this phenomenology is still under debate, especially for the case of information spreading through many channels and platforms. In the present study, we adopt a two-layered network to represent the interactions of multiple channels and propose a Susceptible-Accepted-Recovered information spreading model. Interestingly, our model shows a novel double transition including a continuous transition and a following discontinuous transition in the phase diagram, which originates from two outbreaks between the two layers of the network. Furthermore, we reveal that the key factors are a weak coupling condition between the two layers, a large adoption threshold, and the difference of the degree distributions between the two layers. Moreover, we also test the model in the coupled empirical social networks and find similar results as in the synthetic networks. Then, an edge-based compartmental theory is developed which fully explains all numerical results. Our findings may be of significance for understanding the secondary outbreaks of information in real life.

Year:  2018        PMID: 30180601     DOI: 10.1063/1.5038853

Source DB:  PubMed          Journal:  Chaos        ISSN: 1054-1500            Impact factor:   3.642


  2 in total

Review 1.  Coevolution spreading in complex networks.

Authors:  Wei Wang; Quan-Hui Liu; Junhao Liang; Yanqing Hu; Tao Zhou
Journal:  Phys Rep       Date:  2019-07-29       Impact factor: 25.600

2.  Epidemic dynamics on higher-dimensional small world networks.

Authors:  Haiying Wang; Jack Murdoch Moore; Michael Small; Jun Wang; Huijie Yang; Changgui Gu
Journal:  Appl Math Comput       Date:  2022-01-15       Impact factor: 4.091

  2 in total

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