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
Part of the following topical collections:
  1. Big Data Analytics for Network Management


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.


Communication network Influential nodes Network management systems Fault propagation model 



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).


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© 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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