Skip to main content
Log in

Spin glass approach to the feedback vertex set problem

  • Regular Article
  • Published:
The European Physical Journal B Aims and scope Submit manuscript

Abstract

A feedback vertex set (FVS) of an undirected graph is a set of vertices that contains at least one vertex of each cycle of the graph. The feedback vertex set problem consists of constructing a FVS of size less than a certain given value. This combinatorial optimization problem has many practical applications, but it is in the nondeterministic polynomial-complete class of worst-case computational complexity. In this paper we define a spin glass model for the FVS problem and then study this model on the ensemble of finite-connectivity random graphs. In our model the global cycle constraints are represented through the local constraints on all the edges of the graph, and they are then treated by distributed message-passing procedures such as belief propagation. Our belief propagation-guided decimation algorithm can construct nearly optimal feedback vertex sets for single random graph instances and regular lattices. We also design a spin glass model for the FVS problem on a directed graph. Our work will be very useful for identifying the set of vertices that contribute most significantly to the dynamical complexity of a large networked system.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. S.A. Cook, The complexity of theorem-proving procedures, in Proceedings of the 3rd Annual ACM Symposium on Theory of Computing, edited by P.M. Lewis, M.J. Fischer, J.E. Hopcroft, A.L. Rosenberg, J.W. Thatcher, P.R. Young (New York, ACM, 1971), pp. 151–158

  2. R.M. Karp, Reducibility among combinatorial problems, in Complexity of Computer Computations, edited by E. Miller, J.W. Thatcher, J.D. Bohlinger (New York Plenum Press, 1972), pp. 85–103

  3. M. Garey, D.S. Johnson, Computers and Intractability: A Guide to the Theory of NP-Completeness (Freeman, San Francisco, 1979)

  4. L.W. Beineke, R.C. Vandell, J. Graph Theory 25, 59 (1997)

    Article  MATH  MathSciNet  Google Scholar 

  5. D. Zöbel, SIGOPS Oper. Syst. Rev. 17, 6 (1983)

    Article  Google Scholar 

  6. B. Fiedler, A. Mochizuki, G. Kurosawa, D. Saito, J. Dyn. Diff. Eq. 25, 563 (2013)

    Article  MATH  MathSciNet  Google Scholar 

  7. A. Mochizuki, B. Fiedler, G. Kurosawa, D. Saito, J. Theor. Biol. 335, 130 (2013)

    Article  MathSciNet  Google Scholar 

  8. Y.-Y. Liu, J.-J. Slotine, A.-L. Barabási, Nature 473, 167 (2011)

    Article  ADS  Google Scholar 

  9. Y.-Y. Liu, J.-J. Slotine, A.-L. Barabási, Proc. Natl. Acad. Sci. USA 110, 2460 (2013)

    Article  MathSciNet  ADS  Google Scholar 

  10. G. Bianconi, M. Marsili, J. Stat. Mech.: Theor. Exp. 2005, P06005 (2005)

    Article  Google Scholar 

  11. E. Marinari, R. Monasson, G. Semerjian, Europhys. Lett. 73, 8 (2006)

    Article  MathSciNet  ADS  Google Scholar 

  12. E. Marinari, G. Semerjian, J. Stat. Mech.: Theor. Exp. 2006, P06019 (2006)

    Article  MathSciNet  Google Scholar 

  13. E. Marinari, G. Semerjian, V. Van Kerrebroeck, Phys. Rev. E 75, 066708 (2007)

    Article  MathSciNet  ADS  Google Scholar 

  14. G. Bianconi, N. Gulbahce, J. Phys. A 41, 224003 (2008)

    Article  MathSciNet  ADS  Google Scholar 

  15. Da-Ren He, Zong-Hua Liu, Bing-Hong Wang, Complex Systems and Complex Network (Higher Education Press, Beijing, 2009)

  16. M. Bayati, C. Borgs, A. Braunstein, J. Chayes, Phys. Rev. Lett. 101, 037208 (2008)

    Article  ADS  Google Scholar 

  17. M. Bailly-Bechet, C. Borgs, A. Braunstein, J. Cheyes, A. Dagkessamanskaia, J.-M. Francois, R. Zecchina, Proc. Natl. Acad. Sci. USA 108, 882 (2011)

    Article  ADS  Google Scholar 

  18. I. Biazzo, A. Braunstein, R. Zecchina, Phys. Rev. E 86, 026706 (2012)

    Article  ADS  Google Scholar 

  19. H.A. Bethe, Proc. R. Soc. London A 150, 552 (1935)

    Article  MATH  ADS  Google Scholar 

  20. R. Peierls, Proc. R. Soc. London A 154, 207 (1936)

    Article  MATH  ADS  Google Scholar 

  21. R. Peierls, Proc. Camb. Phil. Soc. 32, 477 (1936)

    Article  MATH  ADS  Google Scholar 

  22. M. Mézard, A. Montanari, Information, Physics, and Computation (Oxford Univ. Press, New York, 2009)

  23. H.-J. Zhou, C. Wang, J. Stat. Phys. 148, 513 (2012)

    Article  MATH  MathSciNet  ADS  Google Scholar 

  24. J.-Q. Xiao, H.-J. Zhou, J. Phys. A 44, 425001 (2011)

    Article  MathSciNet  ADS  Google Scholar 

  25. H.-J. Zhou, C. Wang, J.-Q. Xiao, Z. Bi, J. Stat. Mech.: Theo. Exp. 2011, L12001 (2011)

    Article  Google Scholar 

  26. R. Kikuchi, Phys. Rev. 81, 988 (1951)

    Article  MATH  MathSciNet  ADS  Google Scholar 

  27. G. An, J. Stat. Phys. 52, 727 (1988)

    Article  MATH  ADS  Google Scholar 

  28. M. Mézard, G. Parisi, Eur. Phys. J. B 20, 217 (2001)

    Article  ADS  Google Scholar 

  29. S. Bau, N.C. Wormald, S.-M. Zhou, Random Struct. Alg. 21, 397 (2002)

    Article  MATH  MathSciNet  Google Scholar 

  30. V. Bafna, P. Berman, T. Fujito, SIAM J. Discrete Math. 12, 289 (1999)

    Article  MATH  MathSciNet  Google Scholar 

  31. A. Lucas, arXiv:1302.5841 (2013)

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hai-Jun Zhou.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Zhou, HJ. Spin glass approach to the feedback vertex set problem. Eur. Phys. J. B 86, 455 (2013). https://doi.org/10.1140/epjb/e2013-40690-1

Download citation

  • Received:

  • Revised:

  • Published:

  • DOI: https://doi.org/10.1140/epjb/e2013-40690-1

Keywords

Navigation