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Critical phenomena of spreading dynamics on complex networks with diverse activity of nodes.

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Title: Critical phenomena of spreading dynamics on complex networks with diverse activity of nodes.
Authors: Zhou, Li-xin1, Lin, Jie1 jielinfd@163.com, Wang, Yu-qing2, Li, Yan-feng1, Miao, Run-sheng1
Source: Physica A. Nov2018, Vol. 509, p439-447. 9p.
Subjects: Mean field theory, Probability theory, Diffusion of innovations theory, Simulation methods & models, Analytical mechanics
Abstract: In this paper, we propose a new model to investigate the spreading dynamic and critical phenomena on complex networks based on SIR model. Different from previous studies, we combine the effects of activity rate and infected rate on spreading process. Network nodes become active according to different probability correlated with its degree. Active infected nodes can interact all active susceptible neighbors, meanwhile, recover at a certain probability. By means of the mean-field equations, we find the basic reproductive number and critical threshold of spreading dynamic can be explained by the eigenvalues and eigenvectors of the correlation matrix. Furthermore, we utilize analytical and numerical simulations to explore the critical phenomenon and spreading dynamics of homogeneous and heterogeneous networks respectively. Our results indicate that both homogeneous networks and heterogeneous networks of the model exhibit a critical threshold consists of critical activity rate and infection rate in the spreading dynamic. The critical threshold of infection rate is increased by node activity, and node activity also shows a critical phenomenon given certain infection rate. Results validate that our model is a feasible and economical method to control spreading dynamics and promote further application of innovation diffusion, viral marketing in reality. [ABSTRACT FROM AUTHOR]
Copyright of Physica A is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
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  Data: Critical phenomena of spreading dynamics on complex networks with diverse activity of nodes.
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  Data: <searchLink fieldCode="AR" term="%22Zhou%2C+Li-xin%22">Zhou, Li-xin</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Lin%2C+Jie%22">Lin, Jie</searchLink><relatesTo>1</relatesTo><i> jielinfd@163.com</i><br /><searchLink fieldCode="AR" term="%22Wang%2C+Yu-qing%22">Wang, Yu-qing</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Li%2C+Yan-feng%22">Li, Yan-feng</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Miao%2C+Run-sheng%22">Miao, Run-sheng</searchLink><relatesTo>1</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22Physica+A%22">Physica A</searchLink>. Nov2018, Vol. 509, p439-447. 9p.
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– Name: Abstract
  Label: Abstract
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  Data: In this paper, we propose a new model to investigate the spreading dynamic and critical phenomena on complex networks based on SIR model. Different from previous studies, we combine the effects of activity rate and infected rate on spreading process. Network nodes become active according to different probability correlated with its degree. Active infected nodes can interact all active susceptible neighbors, meanwhile, recover at a certain probability. By means of the mean-field equations, we find the basic reproductive number and critical threshold of spreading dynamic can be explained by the eigenvalues and eigenvectors of the correlation matrix. Furthermore, we utilize analytical and numerical simulations to explore the critical phenomenon and spreading dynamics of homogeneous and heterogeneous networks respectively. Our results indicate that both homogeneous networks and heterogeneous networks of the model exhibit a critical threshold consists of critical activity rate and infection rate in the spreading dynamic. The critical threshold of infection rate is increased by node activity, and node activity also shows a critical phenomenon given certain infection rate. Results validate that our model is a feasible and economical method to control spreading dynamics and promote further application of innovation diffusion, viral marketing in reality. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Physica A is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1016/j.physa.2018.06.046
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      – Code: eng
        Text: English
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        PageCount: 9
        StartPage: 439
    Subjects:
      – SubjectFull: Mean field theory
        Type: general
      – SubjectFull: Probability theory
        Type: general
      – SubjectFull: Diffusion of innovations theory
        Type: general
      – SubjectFull: Simulation methods & models
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      – SubjectFull: Analytical mechanics
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      – TitleFull: Critical phenomena of spreading dynamics on complex networks with diverse activity of nodes.
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            NameFull: Wang, Yu-qing
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            NameFull: Li, Yan-feng
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              M: 11
              Text: Nov2018
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              Y: 2018
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