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Efficient methods for computing the reliability polynomials of graphs and complex networks

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Abstract

Various methods have been proposed to evaluate the reliability of a graph, one of the most well known of which is the reliability polynomial, R(G, p). It is assumed that G(V, E) is a simple and unweighted connected graph whose nodes are perfect and edges are operational with an independent probability p. Thus, the edge reliability polynomial is a function of p of the number of network edges. There are various methods for calculating the coefficients of reliability polynomial, all of which are related to their recursive nature, which has led to an increase in their computational complexity. Therefore, if the difference between the number of links and nodes in the network exceeds a certain amount, the exact calculation of the coefficients R(G, p) is practically in the NP-hard complexity class. In this paper, while examining the problems in the previous methods, four new approaches for estimating the coefficients of reliability polynomial are presented. In the first approach, using an iterative method, the coefficients are estimated. This method, on average, has the same accuracy as common methods in the related studies. In addition, the second method as an intelligent scheme for integrating the values of coefficients has been proposed. The values of coefficients for smaller, larger, and finally intermediate indices have been determined with the help of this intelligent approach. Further, as a third proposed method, Benford's law is utilized to combine the coefficients. Finally, in the fourth approach, using the Legendre interpolation method, the coefficients are effectively estimated with an appropriate accuracy. To compare these approaches fairly and accurately with each other, they have been carried out on synthetic and real-world underlying graphs. Then, their efficiency and accuracy have been evaluated, compared, and analyzed according to the experimental results.

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Notes

  1. Mean Squared Error.

  2. Error of coefficient.

References

  1. Di Caprio D et al (2021) A novel ant colony algorithm for solving shortest path problems with fuzzy arc weights. Alex Eng J. https://doi.org/10.1016/j.aej.2021.08.058

    Article  Google Scholar 

  2. Sori AA et al (2021) Fuzzy constrained shortest path problem for location-based online services. Int J Uncertain Fuzz Knowl-Based Syst 29(2):231–248

    Article  MathSciNet  Google Scholar 

  3. Sori AA et al (2020) Elite artificial bees’ colony algorithm to solve robot’s fuzzy constrained routing problem. Comput Intell 36(2):659–681

    Article  Google Scholar 

  4. Sori AA et al (2020) The fuzzy inference approach to solve multi-objective constrained shortest path problem. J Intell Fuzzy Syst 38(4):4711–4720

    Article  Google Scholar 

  5. Y. Khorramzadeh, (2015) Network reliability: theory, estimation, and applications, Ph.D. dissertation, Virginia Tech

  6. Moore EF, Shannon CE (1956) Reliable circuits using less reliable relays. J Franklin Inst 262(3):191–208

    Article  MathSciNet  Google Scholar 

  7. Cowell ER et al (2018) On the exact reliability enhancements of small hammock networks. IEEE Access 6:25411–25426

    Article  Google Scholar 

  8. Rohatinovici NC, Proştean O, Balas VE (2018) On reliability of 3D hammock networks. In: IEEE 12th International Symposium on Applied Computational Intelligence and Informatics (SACI), pp 000149–000154

  9. Robledo F et al (2013) A novel interpolation technique to address the Edge-Reliability problem. In: 5th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT), IEEE, pp 187–192

  10. Burgos JM, Amoza FR (2016) Factorization of network reliability with perfect nodes I: introduction and statements. Discret Appl Math 198:82–90

    Article  MathSciNet  Google Scholar 

  11. Khorramzadeh Y et al (2015) Analyzing network reliability using structural motifs. Phys Rev E 91(4):042814

    Article  Google Scholar 

  12. Eubank S, Youssef M, Khorramzadeh Y (2014) Using the network reliability polynomial to characterize and design networks. J Complex Netw 2(4):356–372

    Article  Google Scholar 

  13. Brown JI, Cox D, Ehrenborg R (2014) The average reliability of a graph. Discret Appl Math 177:19–33

    Article  MathSciNet  Google Scholar 

  14. J.I. Brown and K. Dilcher, On the roots of strongly connected reliability polynomials, Networks: An International Journal, Vol. 54, No. 2, pp.108–116, 2009.

  15. Brown JI, Colbourn CJ (1992) Roots of the reliability polynomials. SIAM J Discret Math 5(4):571–585

    Article  MathSciNet  Google Scholar 

  16. Royle G, Sokal AD (2004) The Brown-Colbourn conjecture on zeros of reliability polynomials is false. J Comb Theory Ser B 91(2):345–360

    Article  MathSciNet  Google Scholar 

  17. Brown JI et al (2014) Inflection points for network reliability. Telecommun Syst 56(1):79–84

    Article  Google Scholar 

  18. Brown JI, Koç Y, Kooij RE (2011) Reliability polynomials crossing more than twice. In: 3rd IEEE International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT), pp 1–6

  19. Page LB, Perry JE (1994) Reliability polynomials and link importance in networks. IEEE Trans Reliab 43(1):51–58

    Article  Google Scholar 

  20. Chen Y et al (2003) A note on reliability polynomials and link importance in networks. In: International Conference on Neural Networks and Signal Processing, Proceedings of the 2003, vol 2, pp 1674–1676, IEEE

  21. Beichl I, Cloteaux B, Sullivan F (2010) An approximation algorithm for the coefficients of the reliability polynomial. Congr Numer 197:143–151

    MathSciNet  MATH  Google Scholar 

  22. Colbourn CJ, Debroni BM, Myrvold WJ (1988) Estimating the coefficients of the reliability polynomial. Congr Numer 62:217–223

    MathSciNet  MATH  Google Scholar 

  23. Harris DG, Sullivan F (2015) Sequential importance sampling algorithms for estimating the all-terminal reliability polynomial of sparse graphs. In: Approximation, Randomization, and Combinatorial Optimization, Algorithms and Techniques (APPROX/RANDOM 2015), Schloss Dagstuhl-Leibniz-Zentrum Fuer Informatik

  24. Harris DG, Sullivan F, Beichl I (2011) Linear algebra and sequential importance sampling for network reliability. In: IEEE Proceedings of the 2011 Winter Simulation Conference (WSC), pp 3339–3347

  25. Rausand M, Hoyland A (2003) System reliability theory: models, statistical methods, and applications. Wiley, London

    MATH  Google Scholar 

  26. Colbourn CJ (1987) The combinatorics of network reliability. Oxford University Press, New York

    Google Scholar 

  27. W. Ellens, (2011) Effective resistance and other graph measures for network robustness, MS thesis, Leiden University

  28. G. Kirchhoff, (1847) Über die Auflösung der Gleichungen, auf welche man bei der Untersuchung der linearen Vertheilung galvanischer Ströme geführt wird. Ann Phys, 148: 497–508

  29. Satyanarayana A, Chang MK (1983) Network reliability and the factoring theorem. Networks 13(1):107–120

    Article  MathSciNet  Google Scholar 

  30. Benford F (1938) The law of anomalous numbers. Proc Am Philos Soc 78:551–572

    MATH  Google Scholar 

  31. L. Zhipeng, C. Lin, W. Huajia, (2004) Discussion on Benford’s law and its applications, Cornell University Library

  32. Hill TP (1995) A statistical derivation of the significant-digit law. Stat Sci 10(4):354–363

    Article  MathSciNet  Google Scholar 

  33. Batra P (2008) Newton’s method and the computational complexity of the fundamental theorem of algebra. Electron Notes Theor Comput Sci 202:201–218

    Article  MathSciNet  Google Scholar 

  34. Choi M, Krishna CM (1989) On measures of vulnerability of interconnection networks. Microelectron Reliab 29(6):1011–1020

    Article  Google Scholar 

  35. Chvátal V (1973) Tough graphs and Hamiltonian circuits. Discret Math 5(3):215–228

    Article  MathSciNet  Google Scholar 

  36. Fiedler M (1973) Algebraic connectivity of graphs. Czechoslov Math J 23(98):298–305

    Article  MathSciNet  Google Scholar 

  37. Baras JS, Hovareshti P (2009) Efficient and robust communication topologies for distributed decision making in networked systems. In: Proceedings of the 48th IEEE Conference on Decision and Control (CDC), pp 3751–3756

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Safaei, F., Akbar, R. & Moudi, M. Efficient methods for computing the reliability polynomials of graphs and complex networks. J Supercomput 78, 9741–9781 (2022). https://doi.org/10.1007/s11227-021-04216-2

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