doi: 10.17586/2226-1494-2021-21-4-490-498


A study of the stability of information and telecommunication networks under conditions of stochastic percolation of nodes

F. L. Shuvaev, K. I. Vitenzon


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Shuvaev F.L., Vitenzon K.I. A study of the stability of information and telecommunication networks under conditions of stochastic percolation of nodes. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2021, vol. 21, no. 4, pp. 490–498 (in Russian). doi: 10.17586/2226-1494-2021-21-4-490-498



Abstract
In-depth studies of the topological properties of information and telecommunication networks contribute to the understanding of their functional capabilities, including stability. The study of the stability of complex networks to failures in operation when their components fail is based on modeling by sequentially removing nodes or edges of the network (percolation). The paper presents a comparative analysis of sequential and stochastic variants of percolation of network nodes and statistical estimates of the complex two-criterion network stability coefficient. During the study, methods for calculating the average path length based on graph theory were used. In the statistical analysis of the network stability, we applied the analysis of variance and pairwise comparisons according to the Tukey criterion, based on the provisions of the theory of mathematical statistics. The simulation is performed using the Barabashi–Albert and Erdős–Rényi random graph models. The difference between the method of stochastic percolation and sequential percolation is shown. The performed statistical analysis proved the influence of the factor changing the structure of networks on their stability due to stochastic percolation. The dynamics of network stability reduction under stochastic percolation for different types of networks is shown. It is revealed that in some cases, for example, in networks with high density, the stochastic percolation method is the most preferable one. The study shows the possible options for assessing the stability of networks without a priori knowledge about the type of connections between nodes and with a priori knowledge about the type of connections between nodes. In the former case, knowing the number of network nodes, one can calculate the limit values of stability, in the same way as if the nodes were deleted accidentally. The latter option can be used to calculate the stability of networks that are subject to random node failures, for example, when diagnosing technical systems.

Keywords: stability, network, analysis of variance, centrality measure, Tukey criterion, percolation

References
  1. Semenov K., Shuvaev F., Vitenzon K. An analysis of the ways to reduce the vulnerability of networks based on the sequential removal of key elements. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2021, vol. 21, № 2, pp. 241–248. (in Russian). https://doi.org/10.17586/2226-1494-2021-21-2-241-248.
  2. Singer Y. Dynamic measure of network robustness. Proc. IEEE 24th Convention of Electrical & Electronics Engineers in Israel, 2006, pp. 366–370. https://doi.org/10.1109/EEEI.2006.321105
  3. Eremeev I., Tatarka M., Shuvaev F., Tsyganov A. Comparative analysis of centrality measures of network nodes based on principal component analysis. Informatics and Automation, 2020, vol. 19, no. 6, pp. 1307–1331. (in Russian). https://doi.org/10.15622/ia.2020.19.6.
  4. Zhou M., Liu J., Wang S., He S. A comparative study of robustness measures for cancer signaling networks. Big Data and Information Analytics, 2017, vol. 2, no. 1, pp. 87–96. https://doi.org/10.3934/bdia.2017011
  5. Liu J., Zhou M., Wang S., Penghui L. A comparative study of network robustness measures. Frontiers of Computer Science, 2017, vol. 11, no. 4, pp. 568–584. https://doi.org/10.1007/s11704-016-6108-z
  6. Holme P. Kim B.J., Yoon C.N., Han S.K. Attack vulnerability of complex networks. Physical Review E. Statistical, Nonlinear, and Soft Matter Physics, 2002, vol. 65, no. 5, pp 056109. https://doi.org/10.1103/PhysRevE.65.056109
  7. Lu Z.M., Li X.F. Attack vulnerability of network controllability. PLoS One, 2016, vol. 11, no. 9, pp. e0162289. https://doi.org/10.1371/journal.pone.0162289
  8. Dong S.J., Mostafizi A., Wang H.Z., Gao J.X., Li X.P. Measuring the topological robustness of transportation networks to disaster-induced failures: a percolation approach. Journal of Infrastructure Systems, 2020, vol. 26, no. 2, pp. 04020009. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000533
  9. Takabe S., Hukushima K. Minimum vertex coverp roblems on random hypergraphs: Replica symmetric solution and a leaf removal algorithm.Physical Review E. Statistical, Nonlinear, and Soft Matter Physics, 2014, vol. 89, no. 6, pp. 062139. https://doi.org/10.1103/PhysRevE.89.062139
  10. Grilo M., Fadigas I., Miranda J., Cunha M., Monteiro R., Pereira H. Robustness in semantic networks based on cliques. Physica A: Statistical Mechanics and its Applications, 2017, vol. 472, pp. 94–102. https://doi.org/10.1016/j.physa.2016.12.087
  11. Watson C.G. Brain Graph. User Guide. Available at: https://cwatson.github.io/files/brainGraph_UserGuide.pdf (accessed: 04.03.2021).
  12. Barabási A.-L. Network Science. Glasgow, Cambridge University Press, 2016, 456 p.
  13. Shuvaev F.L., Tatarka M.V. Analysis of mathematical models of random graphs used in simulation of information and communication networks. Vestnik Sankt-Peterburgskogo universiteta GPS MChS Rossi, 2020, no. 2, pp. 67–77. (in Russian)
  14. Newman M.E.J. Networks an introduction. N.Y.: Oxford University Press Inc., 2010, 1042 p.
  15. Hartmann A., Mézard M. Distribution of diameters for Erdős-Rényi random graphs. Physical Review E, 2018, vol. 97, no. 3, pp. 032128. https://doi.org/10.1103/PhysRevE.97.032128
  16. Chen P-Y., Choudhury S., Hero A. Multi-centrality graph spectral decompositions and their application to cyber intrusion detection. Proc. 41st IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016, pp. 4553–4557. https://doi.org/10.1109/ ICASSP.2016.7472539
  17. Shuvaev F.L., Tatarka M.V. Dynamics of centrality measures of random graph mathematical models. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2020, vol. 20, no. 2, pp. 249–256. (in Russian). https://doi.org/10.17586/2226-1494-2020-20-2-249-256.
  18. Gibson H., Vickers P. Using adjacency matrices to lay out larger small-world networks. Applied Soft Computing, 2016, vol. 42, pp. 80–92. https://doi.org/10.1016/j.asoc.2016.01.036
  19. Csardi G., Nepusz T. The IGRAPH software package for complex network research. InterJournal, Complex Systems, 2006, vol. 1695.
  20. Butts C.T., Hunter D., Handcock M., Bender-deMoll S., Horner J., Wang L., Krivitsky P.N., Knapp B., Bojanowski M., Klumb C. Classes for Relation Data. Available at: https://cran.r-project.org/web/packages/network/network.pdf(accessed: 03.03.2021).
  21. Schoch D. RDocumentation. Available at: https://rdocumentation.org/packages/networkdata(accessed: 03.03.2021).
  22. Jusupov R.M., Petuhov G.B., Sidorov V.N., Gorodeckij V.I., Markov V.M. Statistical Methods for Processing Observation Results. Moscow, USSR Ministry of Defense Publ., 1984, 786 p. (in Russian)
  23. Kobzar' A. Applied Mathematical Statistics. Moscow: Fizmatlit Publ., 2012, 813 p. (in Russian)


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