Second order mean field approach of non-Markovian susceptible-infected model for complex networks

QI Ting; LIN Zhaohua; FENG Mi; TANG Ming

doi:10.3969/j.issn.1000-5641.20202s2001

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QI Ting, LIN Zhaohua, FENG Mi, TANG Ming. Second order mean field approach of non-Markovian susceptible-infected model for complex networks[J]. Journal of East China Normal University (Natural Sciences), 2021, (1): 144-151. doi: 10.3969/j.issn.1000-5641.20202s2001

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QI Ting, LIN Zhaohua, FENG Mi, TANG Ming. Second order mean field approach of non-Markovian susceptible-infected model for complex networks[J]. Journal of East China Normal University (Natural Sciences), 2021, (1): 144-151. doi: 10.3969/j.issn.1000-5641.20202s2001

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Second order mean field approach of non-Markovian susceptible-infected model for complex networks

doi: 10.3969/j.issn.1000-5641.20202s2001

QI Ting¹,
LIN Zhaohua²,
FENG Mi³,
TANG Ming^{2
,
,}

1.
School of Communication and Electronic Engineering, East China Normal University, Shanghai　200241, China
2.
School of Physics and Electronic Science, East China Normal University, Shanghai　200241, China
3.
Department of Physics, Hong Kong Baptist University, Hong Kong　999077, China

Received Date: 2020-03-03
Publish Date: 2021-01-27

Abstract

Abstract

The objective of this paper is to propose a mathematical theory that can describe the non-Markovian characteristics of the network spreading process, thereby establishing theoretical support for controlling the propagation of diseases or rumors in the real world. According to the second-order mean-field approximation method and the concept of idle edges, a series of partial differential equations are presented that can be used to solve the non-Markovian spreading dynamics of a susceptible-infected (SI) model in complex networks. By comparing the simulation outputs with the theoretical results, this mathematical method can accurately predict the spreading process of the SI model on complex networks. The theory, moreover, can be used to predict the average time for a single node to be infected. The correctness and accuracy of the theory is verified by experimental simulation results.
- complex network,
- spreading dynamics,
- non-Markovian,
- second order mean field theory

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References(27)

References

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