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Journal of Network and Systems Management

, Volume 28, Issue 1, pp 21–34 | Cite as

Identify Critical Nodes in Network Cascading Failure Based on Data Analysis

  • Bingchun Wang
  • Zhirui Zhang
  • Xiaogang QiEmail author
  • Lifang Liu
Article
  • 112 Downloads
Part of the following topical collections:
  1. Big Data Analytics for Network Management

Abstract

In communication networks, the cascading failure, which is initiated by influential nodes, may cause local paralysis of communication networks and make network management systems face big challenges in both fault location and the rational use of maintenance resource. As network failure is inevitable, how to find the fragile nodes and the root cause of cascade failure has been recognized as an important research problem in both academia and industry. In this paper, we focus on the problem of identifying critical nodes when cascading failures occur in communication networks. Based on the Barabási–Albert (BA) model, which is used to generate the scale-free network, we design a reasonable global model of load redistribution for the communication network, and we also find that the betweenness centrality can accurately reflect the scale of cascading failure, and the closeness centrality is negatively correlated to the frequency of failure participation, by (1) establishing a reasonable model of fault propagation, (2) extracting and analyzing the dataset derived from the topology information. Simulation results demonstrate that our model can effectively identify critical nodes of networks and the global redistribution model is more robust than other existing models.

Keywords

Communication network Influential nodes Network management systems Fault propagation model 

Notes

Acknowledgements

Project supported by the National Natural Science Foundation of China (Grants Nos. 61877067, 61572435), Joint fund project the Ministry of Education—the China Mobile (No. MCM20170103), Xi’an Science and Technology Innovation Project (Grants No.201805029YD7CG13-6), Ningbo Natural Science Foundation (Grants Nos. 2016A610035, 2017A610119).

References

  1. 1.
    Dusia, A., Sethi, A.S.: Recent advances in fault localization in computer networks. IEEE Commun. Surv. Tutor. 18(4), 3030–3051 (2016)CrossRefGoogle Scholar
  2. 2.
    Strogatz, S.: Small-world networks. In: Lecture Notes in Physics (1999).  https://doi.org/10.1007/BFb0105015
  3. 3.
    Barabási, A.L., Bonabeau, E.: Scale-free networks. Sci. Am. (2003).  https://doi.org/10.1038/scientificamerican0503-60 CrossRefGoogle Scholar
  4. 4.
    Enrico, Z., Giovanni, S.: Component criticality in failure cascade processes of network systems. Risk Anal. 31(8), 1196–1210 (2011)CrossRefGoogle Scholar
  5. 5.
    Jian, Y., Liu, E., Wang, Y., Zhang, Z., Lin, C.: Scale-free model for wireless sensor networks. In: 2013 IEEE Wireless Communications and Networking Conference (WCNC), Shanghai, pp. 2329–2332 (2013)Google Scholar
  6. 6.
    Sohn, I.: Small-world and scale-free network models for IoT systems. Mob. Inf. Syst. 61, 1–9 (2017)Google Scholar
  7. 7.
    Tan, M.S.A., Ujum, E.A., Ratnavelu, K.: Social network analysis of character interaction in the Stargate and Star Trek television series. Int. J. Mod. Phys. C 28, 1750017 (2017)CrossRefGoogle Scholar
  8. 8.
    Fan, Z., Duan, W., Zhang, P., Qiu, X.: Weighted social networks for a large scale artificial society. Mod. Phys. Lett. B 30(02), 1550276 (2016)CrossRefGoogle Scholar
  9. 9.
    Barthelemy, M.: Betweenness centrality. Morphogenesis of Spatial Networks. Lecture Notes in Morphogenesis, pp. 51–73. Springer, Cham (2018)zbMATHGoogle Scholar
  10. 10.
    Chen, D., Lü, L., Shang, M., Zhang, Y., Zhou, T.: Identifying influential nodes in complex networks. Phys. A 391(4), 1777–1787 (2012)CrossRefGoogle Scholar
  11. 11.
    Ghanbari, R., Jalili, M., Yu, X.: Correlation of cascade failures and centrality measures in complex networks. Future Gener. Comput. Syst. (2017).  https://doi.org/10.1016/j.future.2017.09.007 CrossRefGoogle Scholar
  12. 12.
    Rhouma, D., Romdhane, L.B.: A new centrality measure for identifying influential nodes in social networks. In: Tenth International Conference on Machine Vision (2018).  https://doi.org/10.1117/12.2309872
  13. 13.
    Jiali, D., Fanghua, Y., Wuhui, C., Jiajing, W.: Identifying influential nodes in complex networks via semi-local centrality. In: 2018 IEEE International Symposium on Circuits and Systems (ISCAS), Florence (2018).  https://doi.org/10.1109/ISCAS.2018.8351889
  14. 14.
    Shao, Z., Liu, S., Zhao, Y., et al.: Identifying influential nodes in complex networks based on neighbours and edges. Peer-to-Peer Netw. Appl. (2018).  https://doi.org/10.1007/s12083-018-0681-x CrossRefGoogle Scholar
  15. 15.
    Lalou, M., Tahraoui, M.A., Kheddouci, H.: The critical node detection problem in networks: a survey. Comput. Sci. Rev. 28, 92–117 (2018)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Duan, D., Wu, J., Deng, H., Sha, F., Wu, X., Tan, Y.: Cascading failure model of complex networks based on tunable load redistribution. Syst. Eng. Theory Pract. 33(1), 203–208 (2013)Google Scholar
  17. 17.
    Han, L., Liu, B., Deng, Y., Wang, Q., Yin, R., Liu, H.: Cascading Failure Model of Weighted Scale-Free Networks. J. Softw. 28(10), 2769–2781 (2017)MathSciNetzbMATHGoogle Scholar
  18. 18.
    Erdos, P., Rényi, A.: On random graphs I. Publ. Math. (1959).  https://doi.org/10.1109/ICSMC.2006.384625 CrossRefzbMATHGoogle Scholar
  19. 19.
    Watts, D.J., Strogatz, S.H.: Collective dynamics of’small-world’ networks. Nature 393, 440–442 (1998)CrossRefGoogle Scholar
  20. 20.
    Barabasi, A.L., Albert, R.: Emergence of scaling in random networks. Science 286, 509–512 (1999)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Ghazzali, N., Ouellet, A.: Comparative Study of Centrality Measures on Social Networks. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-67633-3_1 CrossRefGoogle Scholar
  22. 22.
    Kendall, M.: a new measure of rank correlation. Biometrika 30(1–2), 81–89 (1938)CrossRefGoogle Scholar
  23. 23.
    Schubert, E., Sander, J., Ester, M., Kriegel, H.P., Xu, X.: Dbscan revisited, revisited: why and how you should (still) use dbscan. ACM Trans. Database Syst. 42(3), 1–21 (2017)MathSciNetCrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  • Bingchun Wang
    • 1
  • Zhirui Zhang
    • 2
  • Xiaogang Qi
    • 1
    • 4
    Email author
  • Lifang Liu
    • 3
    • 4
  1. 1.School of Mathematics and StatisticsXidian UniversityXi’anPeople’s Republic of China
  2. 2.China Mobile Group Co., Ltd.Shanxi Co., Ltd.TaiyuanPeople’s Republic of China
  3. 3.School of Computer Science and TechnologyXidian UniversityXi’anPeople’s Republic of China
  4. 4.Xidian-Ningbo Information Technology InstituteNingboPeople’s Republic of China

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