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.
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References
Dusia, A., Sethi, A.S.: Recent advances in fault localization in computer networks. IEEE Commun. Surv. Tutor. 18(4), 3030–3051 (2016)
Strogatz, S.: Small-world networks. In: Lecture Notes in Physics (1999). https://doi.org/10.1007/BFb0105015
Barabási, A.L., Bonabeau, E.: Scale-free networks. Sci. Am. (2003). https://doi.org/10.1038/scientificamerican0503-60
Enrico, Z., Giovanni, S.: Component criticality in failure cascade processes of network systems. Risk Anal. 31(8), 1196–1210 (2011)
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)
Sohn, I.: Small-world and scale-free network models for IoT systems. Mob. Inf. Syst. 61, 1–9 (2017)
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)
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)
Barthelemy, M.: Betweenness centrality. Morphogenesis of Spatial Networks. Lecture Notes in Morphogenesis, pp. 51–73. Springer, Cham (2018)
Chen, D., Lü, L., Shang, M., Zhang, Y., Zhou, T.: Identifying influential nodes in complex networks. Phys. A 391(4), 1777–1787 (2012)
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
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
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
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
Lalou, M., Tahraoui, M.A., Kheddouci, H.: The critical node detection problem in networks: a survey. Comput. Sci. Rev. 28, 92–117 (2018)
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)
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)
Erdos, P., Rényi, A.: On random graphs I. Publ. Math. (1959). https://doi.org/10.1109/ICSMC.2006.384625
Watts, D.J., Strogatz, S.H.: Collective dynamics of’small-world’ networks. Nature 393, 440–442 (1998)
Barabasi, A.L., Albert, R.: Emergence of scaling in random networks. Science 286, 509–512 (1999)
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
Kendall, M.: a new measure of rank correlation. Biometrika 30(1–2), 81–89 (1938)
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)
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).
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Wang, B., Zhang, Z., Qi, X. et al. Identify Critical Nodes in Network Cascading Failure Based on Data Analysis. J Netw Syst Manage 28, 21–34 (2020). https://doi.org/10.1007/s10922-019-09499-8
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DOI: https://doi.org/10.1007/s10922-019-09499-8