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Activity of nodes reshapes the critical threshold of spreading dynamics in complex networks.

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Title: Activity of nodes reshapes the critical threshold of spreading dynamics in complex networks.
Authors: Liu, Chen1 liuchen.chn@hotmail.com, Zhou, Li-xin1, Fan, Chong-jun1, Huo, Liang-an1, Tian, Zhan-wei2
Source: Physica A. Aug2015, Vol. 432, p269-278. 10p.
Subjects: Epidemiological models, Disease susceptibility, Probability theory, Computer simulation, Mean field theory
Abstract: In this paper, we investigate spreading dynamics on complex networks with active nodes based on SIR (Susceptible–Infected–Removed) model. Different from previous studies, each node of the network rotates between active state and inactive state according to certain probabilities. An active susceptible node can be infected by all its infected neighbors, while an inactive susceptible node can only be infected by its active infected neighbors. By means of ​mean-field approach and numerical simulations, we explore the critical phenomenon by the combined effects of activity rate and infection rate on spreading dynamics. We show that the critical threshold of infection rate is increased by node activity, and node activity also shows a critical phenomenon given certain infection rate. On the whole, there exists a critical curve consists of pairs of critical activity rate and infection rate. We also analyze theoretically the impact of activity rate and infection rate on the final size of spreading dynamics, which is verified by numerical simulations. This work complements our understanding of spreading dynamics with active nodes and may be used to develop more feasible and more economical methods to control spreading dynamics. [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.)
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  Data: Activity of nodes reshapes the critical threshold of spreading dynamics in complex networks.
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  Data: <searchLink fieldCode="AR" term="%22Liu%2C+Chen%22">Liu, Chen</searchLink><relatesTo>1</relatesTo><i> liuchen.chn@hotmail.com</i><br /><searchLink fieldCode="AR" term="%22Zhou%2C+Li-xin%22">Zhou, Li-xin</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Fan%2C+Chong-jun%22">Fan, Chong-jun</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Huo%2C+Liang-an%22">Huo, Liang-an</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Tian%2C+Zhan-wei%22">Tian, Zhan-wei</searchLink><relatesTo>2</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22Physica+A%22">Physica A</searchLink>. Aug2015, Vol. 432, p269-278. 10p.
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  Data: <searchLink fieldCode="DE" term="%22Epidemiological+models%22">Epidemiological models</searchLink><br /><searchLink fieldCode="DE" term="%22Disease+susceptibility%22">Disease susceptibility</searchLink><br /><searchLink fieldCode="DE" term="%22Probability+theory%22">Probability theory</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+simulation%22">Computer simulation</searchLink><br /><searchLink fieldCode="DE" term="%22Mean+field+theory%22">Mean field theory</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: In this paper, we investigate spreading dynamics on complex networks with active nodes based on SIR (Susceptible–Infected–Removed) model. Different from previous studies, each node of the network rotates between active state and inactive state according to certain probabilities. An active susceptible node can be infected by all its infected neighbors, while an inactive susceptible node can only be infected by its active infected neighbors. By means of ​mean-field approach and numerical simulations, we explore the critical phenomenon by the combined effects of activity rate and infection rate on spreading dynamics. We show that the critical threshold of infection rate is increased by node activity, and node activity also shows a critical phenomenon given certain infection rate. On the whole, there exists a critical curve consists of pairs of critical activity rate and infection rate. We also analyze theoretically the impact of activity rate and infection rate on the final size of spreading dynamics, which is verified by numerical simulations. This work complements our understanding of spreading dynamics with active nodes and may be used to develop more feasible and more economical methods to control spreading dynamics. [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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        Value: 10.1016/j.physa.2015.03.054
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        Text: English
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        Type: general
      – SubjectFull: Disease susceptibility
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      – SubjectFull: Probability theory
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      – SubjectFull: Computer simulation
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      – SubjectFull: Mean field theory
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      – TitleFull: Activity of nodes reshapes the critical threshold of spreading dynamics in complex networks.
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              Text: Aug2015
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