Skip to main content
SpringerLink
Log in

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

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  1. Lao S, Wang J, Bai L (2016) Review of the interdependent networks. J Natl Univ Def Technol 1:122–128

    Google Scholar 

  2. Zhang X (2015) Multilayer networks science: concepts, theories and data. Complex Syst Complex Sci 12(02):103–107

    Google Scholar 

  3. Liu Z, Zhao S, Chen J, Zhang Y, Chen X (2017) A novel immune strategy based on node importance. J Nanjing Univ (Natural Sciences) 53(02):350–356

    Google Scholar 

  4. Buldyrev SV, Havlin S, Parshani R et al (2010) Catastrophic cascade of failures in interdependent networks. Nature 464(7291):1025

    Article  Google Scholar 

  5. Zhao D, Wang L, Li S et al (2014) Immunization of epidemics in multiplex networks. PLoS ONE 9(11):e112018–e112018

    Article  Google Scholar 

  6. Dickison M, Havlin S, Stanley HE (2012) Epidemics on interconnected networks. Phys Rev E Stat Phys Plasmas Fluids Relat Interdiscip Top 85(2):1380–1404

    Google Scholar 

  7. Zhang Y, Xu J (2015) A rumor spreading model considering the cumulative effects of memory. Discrete Dyn Nat Soc 2015:1–11

    MathSciNet  MATH  Google Scholar 

  8. Du Y, Gao C, Hu Y et al (2014) A new method of identifying influential nodes in complex networks based on TOPSIS. Phys A: Stat Mech Appl 399:57–69

    Article  Google Scholar 

  9. Qin L, Yang Z-L, Huang S-G (2015) Synthesis evaluation method for node importance in complex networks. Comput Sci 42(2):60–64

    Google Scholar 

  10. Han H, Yang R, Wang Z (2017) Risk propagation model of two-layer coupled networks under protection strategy. Syst Eng Electron 39(6):1298–1303

    Google Scholar 

  11. Wang J, Shi L, Wang Z (2018) Research on node importance ranking of multilayer network. Appl Res Comput 35(06):1742–1746

    Google Scholar 

  12. Wang G (2019) Small objects detection based on multi-scale feature fusion. Acta Microscopica 28(1):76–81

    Google Scholar 

  13. Du W, Li Y-Z, An X, Ma C-X (2016) A new class of two layers public traffic coupled network model. J Transp Syst Eng Inf Technol 16(4):131–138

    Google Scholar 

  14. Yang LS, Junde W, Liang B (2016) Review of interdependent networks. J Natl Univ Def Technol 1:122–128

    Google Scholar 

  15. Qin W, Fan T (2017) Modeling and analysis of information propagation model of online/offline network based on coupled network. In: Proceedings of Conference on Chinese Conference on Trusted Computing and Information Security, pp 16–25

  16. Zeng J, Yang J, Chen Q (2016) Behavior of individual differences in human knowledge creation in the open source software community. Math Pract Theory 46(13):1–14

    MathSciNet  Google Scholar 

  17. Zhu ZQ, Liu CJ, Wu JL et al (2014) The influence of human heterogeneity to information spreading. J Stat Phys 154(6):1569–1577

    Article  Google Scholar 

  18. Zhang Z, Fan T (2014) Methods with immunization strategies popularity and node heterogeneity. J Hebei Acad Sci 31(02):90–93

    Google Scholar 

  19. Zhao W, Fan T, Nie Y, Wu F, Wen H (2018) Research on attribute dimension partition based on SVM classifying and MapReduce. Wirel Pers Commun 102(4):2759–2774

    Article  Google Scholar 

  20. Zhao W, Yin Z, Fan T, Luo J (2019) Research on influence spread of scientific research team based on scientific factor quantification of big data. Int J Distrib Sens Netw. https://doi.org/10.1177/1550147719842158

    Article  Google Scholar 

  21. Saaty TL (2008) Decision making with the analytic hierarchy process. Int J Serv Sci 1(1):83–98

    Google Scholar 

Download references

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).

Author information

Authors and Affiliations

  1. School of Information Science and Technology, Shijiazhuang Tiedao University, Shijiazhuang, Hebei, China

    Tongrang Fan, Wanting Qin, Wenbin Zhao & Jianmin Wang

  2. Hebei Institute of Science and Technology Information, Shijiazhuang, Hebei, China

    Feng Wu

Authors
  1. Tongrang Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wanting Qin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wenbin Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Feng Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jianmin Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wenbin Zhao.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fan, T., Qin, W., Zhao, W. et al. A two-layer SIR information propagation model with heterogeneity based on coupled network. J Supercomput 76, 1657–1679 (2020). https://doi.org/10.1007/s11227-019-03020-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-019-03020-3

Keywords

Search

Navigation