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A two-layer SIR information propagation model with heterogeneity based on coupled network

  • Tongrang Fan
  • Wanting Qin
  • Wenbin ZhaoEmail author
  • Feng Wu
  • Jianmin Wang
Article
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Abstract

The spreading of information in network is different from epidemics in the population; meanwhile, the node is heterogeneous, and the structure is going in the direction of double or even multi-layer. It is of great practical significance to study the anti-risk capability of coupled network. Based on the subjective heterogeneity and memory effect heterogeneity, a two-layer SIR information propagation model is constructed and an important node selection method for the coupled network based on technique for order preference by similarity to an ideal solution (TOPSIS) is proposed. The effectiveness of the constructed model and the proposed method is verified by simulation experiment which selects the important nodes as the immune nodes of TOPSIS immunization strategy and adopts random immunization strategy, partial nodes immune layer strategy and TOPSIS immunization strategy on BA_BA, WS_WS and BA_WS coupled network. The experimental results show that subjective heterogeneity can hinder the dissemination of information, while the memory effect heterogeneity can facilitate the dissemination of information. In addition, different immune strategies have different effects on different coupled networks, for example, the TOPSIS immune strategy has the best effect in BA_BA network.

Keywords

Coupled network Subjective heterogeneity Memory effect heterogeneity TOPSIS 

Notes

Acknowledgements

The authors acknowledge the National Natural Science Foundation of China (Grant: 61373160), the Standardization Processing and Application System Development of Science and Technology’s Big Data (Grant: 17210113D) and Science and Technology Resource Survey, Statistical Analysis and System Development (Grant: 179676334D).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Tongrang Fan
    • 1
  • Wanting Qin
    • 1
  • Wenbin Zhao
    • 1
    Email author
  • Feng Wu
    • 2
  • Jianmin Wang
    • 1
  1. 1.School of Information Science and TechnologyShijiazhuang Tiedao UniversityShijiazhuangChina
  2. 2.Hebei Institute of Science and Technology InformationShijiazhuangChina

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