Skip to main content
SpringerLink
Log in

Cascading failure model for the mitigating edge failure of scale-free networks

  • Published:
Pramana Aims and scope Submit manuscript

Abstract

By studying the classical betweenness and the universal degree methods, we put forward a new model to control the spread of cascading failure on scale-free networks. The new model is based on defining the load of an edge with respect to the betweenness centrality of the two connected nodes. The iterative process of a cascading failure on scale-free networks is analysed by removing one edge. We find that the proposed new model can control the spread of cascading failure more significantly. To make our conclusions more convincing, we have explored the performance of new models in real network by the power grid of the western United States. We further give the following reasonable explanations: First, the reason why the new model shows a more stable performance than the others has been explained. Secondly, we have explored the reason why the new model shows different advantages depending on the load for different networks and lastly, we have studied the exact difference between these two methods and the network structure. This paper might be useful for preventing and mitigating the cascading failure in real life.

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

Similar content being viewed by others

References

  1. J J Wu, H J Sun and Z Y Gao, Physica A 386, 407 (2007)

    Article  ADS  Google Scholar 

  2. W Wang, M Tang, H E Stanley and L A Braunstein, Rep. Prog. Phys. 80, 036603 (2017)

    Article  ADS  Google Scholar 

  3. J Ma, W Han, Q Guo and Z Wang, Physica A 456, 281 (2016)

    Article  ADS  Google Scholar 

  4. J Ma, W Han, Q Guo, Z Wang and S Zhang, Int. J. Mod. Phys. C 27, 1650054 (2016)

    Article  ADS  Google Scholar 

  5. W Du, B Liang, Y Gang, O Lordan and X Cao, Chin. J. Aeronaut. 30, 330 (2017)

    Article  Google Scholar 

  6. J Ma, L Wang, S Li, C Duan and Y Liu, Mod. Phys. Lett. B 32, 1850054 (2018)

    Article  ADS  Google Scholar 

  7. B Wu and H Shen, Int. J. Inf. Manag. 35, 702 (2015)

    Article  Google Scholar 

  8. B Wu and H Shen, Int. J. Mod. Phys. C 27, 1650072 (2016)

    Article  ADS  Google Scholar 

  9. J Ma, H Wang, Z Zhang, Y Zhang, C Duan, Z Qi and Y Liu, Int. J. Mod. Phys. B 32, 1850155 (2018)

    Article  ADS  Google Scholar 

  10. B Wu, K Chen and H Shen, Proceedings of the International Conference on Computing, Networking and Communication (ICNC) (Silicon Valley, USA, 2017) p. 329

  11. J Cai, Y Wang, Y Liu, J Z Luo, W Wei and X Xu, Future Gener. Comput. Syst. 87, 765 (2018)

    Article  Google Scholar 

  12. Y Zhao, N Cao, Z Qi, G Li and P Liu, IEEE Access 6, 23800 (2018)

    Article  Google Scholar 

  13. J Ma, W Han, Q Guo and S Zhang, Int. J. Mod. Phys. C 27, 1650028 (2016)

    Article  ADS  Google Scholar 

  14. Y Zhu, Z Zhong, W X Zheng and D Zhou, IEEE Trans. Syst. Man Cybern. Syst. 48(12), 2035 (2017), https://doi.org/10.1109/TSMC.2017.2723038

    Article  Google Scholar 

  15. Y Zhu, L Zhang and W X Zheng, IEEE Trans. Ind. Electron. 63, 1876 (2016)

    Article  Google Scholar 

  16. Y Zhu, Z Zhong, M V Basin and D Zhou, IEEE Trans. Autom. Control 63(10), 3456 (2018), https://doi.org/10.1109/TAC.2018.2797173

    Article  Google Scholar 

  17. S Wang, Pramana – J. Phys. 90: 25 (2018)

    Article  ADS  Google Scholar 

  18. J Xiang, T Hu, Y Zhang, K Hu, Y Tang, Y Gao and K Deng, Pramana – J. Phys. 87: 84 (2016)

    Article  ADS  Google Scholar 

  19. F Nian and W Liu, Pramana – J. Phys. 86, 1209 (2016)

    Article  ADS  Google Scholar 

  20. C W Duan, L X Hu and J L Ma, J. Mater. Chem. A 6, 6309 (2018)

    Article  Google Scholar 

  21. F Chaoqi, W Ying and W Xiaoyang, Physica A 482, 317 (2017)

    Article  ADS  MathSciNet  Google Scholar 

  22. P Crucitti, V Latora and M Marchiori, Phys. Rev. E 69, 045104 (2004)

    Article  ADS  Google Scholar 

  23. H J Li, Y Wang, L Y Wu, Z P Liu, L Chen and X S Zhang, Europhys. Lett. 97, 48005 (2012)

    Article  ADS  Google Scholar 

  24. H J Li, Y Wang, L Y Wu, J Zhang and X S Zhang, Phys. Rev. E 86, 016109 (2012)

    Article  ADS  Google Scholar 

  25. H J Li, Z Bu, A Li, Z Liu and Y Shi, IEEE Trans. Knowl. Data Eng. 28, 2349 (2016)

    Article  Google Scholar 

  26. M Tang, Z Liu, X Liang and P M Hui, Phys. Rev. E 80, 026114 (2009)

    Article  ADS  Google Scholar 

  27. M Tang, Z Liu and B Li, Europhys. Lett. 87, 18005 (2009)

    Article  ADS  Google Scholar 

  28. M Tang and T Zhou, Phys. Rev. E 84, 026116 (2011)

    Article  ADS  Google Scholar 

  29. R Ding, N Ujang, H B Hamid, M S A Manan, R Li and J Wu, Physica A 479, 71 (2017)

    Article  ADS  Google Scholar 

  30. R Ding, N Ujang, H B Hamid and J Wu, PLoS ONE 10, e0139961 (2015)

    Article  Google Scholar 

  31. J Xiang, T Hu, Y Zhang, K Hu, J M Li, X K Xu, C C Liu and S Chen, Physica A 443, 451 (2016)

    Article  ADS  Google Scholar 

  32. J Xiang, Y N Tang, Y Y Gao, Y Zhang, K Deng, X K Xu and K Hu, Physica A 432, 127 (2015)

    Article  ADS  Google Scholar 

  33. J Zhang, X B Cao, W B Du and K Q Cai, Physica A 18, 3922 (2010)

    Article  ADS  Google Scholar 

  34. J Xiang, Y N Tang, Y Y Gao, L Liu, Y Hao, J M Li, Y Zhang and S Chen, Physica A 491, 693 (2018)

    Article  ADS  Google Scholar 

  35. W B Du, X L Zhou, O Lordan, Z Wang, C Zhao and Y B Zhu, Transp. Res. E Log. 89, 108 (2016)

    Article  Google Scholar 

  36. W B Du, X L Zhou, Y B Zhu and Z Zheng, Chaos Solitons Fractals 80, 56 (2015)

    Article  ADS  Google Scholar 

  37. A E Motter, Phys. Rev. Lett. 93, 098701 (2004)

    Article  ADS  Google Scholar 

  38. J Gao, S V Buldyrev, H E Stanley and S Havlin, Nat. Phys. 8, 40 (2011)

    Article  Google Scholar 

  39. X Huang, J Gao, S V Buldyrev, S Havlin and H E Stanley, Phys. Rev. E 83, 065101 (2011)

    Article  ADS  Google Scholar 

  40. J W Wang and L L Rong, Saf. Sci. 47, 1332 (2009)

    Article  Google Scholar 

  41. J W Wang and L L Rong, Physica A 388, 1731 (2009)

    Article  ADS  Google Scholar 

  42. W X Wang and G Chen, Phys. Rev. E 77, 026101 (2008)

    Article  ADS  Google Scholar 

  43. B Mirzasoleiman, M Babaei, M Jalili and M Safari, Phys. Rev. E 84, 046114 (2011)

    Article  ADS  Google Scholar 

  44. H R Liu, Y L Hu, R R Yin and Y J Deng, Neurocomputing 260, 443 (2017)

    Article  Google Scholar 

  45. B Derudder, X Liu, C Kunaka and M Roberts, J. Maps 10, 47 (2014)

    Article  Google Scholar 

  46. J Wang, Y Li, J Liu, K He and P Wang, PLoS ONE 8, e80178 (2013)

    Article  ADS  Google Scholar 

  47. S H Lee and P Holme, Phys. Rev. Lett. 108, 128701 (2012)

    Article  ADS  Google Scholar 

  48. A Erath, M Löchl and K W Axhausen, Netw. Spat. Econ. 9, 379 (2009)

    Article  MathSciNet  Google Scholar 

  49. Y Liu, X Luo, R K C Chang and J Su, IEEE J. Sel. Areas Commun. 31, 1147 (2013)

    Article  Google Scholar 

  50. A L Barabási and R Albert, Science 286, 509 (1999)

    Article  ADS  MathSciNet  Google Scholar 

  51. M E J Newman, Phys. Rev. E 64, 016132 (2001)

    Article  ADS  Google Scholar 

  52. L Zhao, K Park, Y C Lai and N Ye, Phys. Rev. E 72, 025104 (2005)

    Article  ADS  Google Scholar 

  53. J Wang, L Rong, L Zhang and Z Zhang, Physica A 387, 6671 (2008)

    Article  ADS  Google Scholar 

  54. A G Phadke and J S Thorp, Computer relaying for power systems, 2nd edn (Research Studies Press, Chichester, 1988)

Download references

Acknowledgements

This research was supported by the Shijiazhuang Science and Technology Research and Development Project (Grant No. 185460135), the Natural Science Foundation of Hebei Province (Grant No. E2018502054), the Doctoral Scientific Research Fund of Hebei University of Science and Technology (Grant No. 1181323) and the Science and Technology Project of Hebei Province (Grant No. 17211903D).

Author information

Authors and Affiliations

  1. College of Mathematics and Information Science, Hebei Normal University, Shijiazhuang,  050024, China

    Zhichao Ju & Jianjun Xie

  2. School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang,  050018, China

    Jinlong Ma, Yanpeng Wang & Huimin Cui

  3. Department of Environmental Science and Engineering, North China Electric Power University, Baoding,  071003, China

    Congwen Duan

Authors
  1. Zhichao Ju
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jinlong Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jianjun Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yanpeng Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Huimin Cui
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Congwen Duan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jinlong Ma.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ju, Z., Ma, J., Xie, J. et al. Cascading failure model for the mitigating edge failure of scale-free networks. Pramana - J Phys 92, 62 (2019). https://doi.org/10.1007/s12043-019-1720-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12043-019-1720-8

Keywords

PACS Nos

Search

Navigation