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Uncovering evolutionary ages of nodes in complex networks

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Abstract

In a complex network, different groups of nodes may have existed for different amounts of time. To detect the evolutionary history of a network is of great importance. We present a spectral-analysis based method to address this fundamental question in network science. In particular, we find that there are complex networks in the real-world for which there is a positive correlation between the eigenvalue magnitude and node age. In situations where the network topology is unknown but short time series measured from nodes are available, we suggest to uncover the network topology at the present (or any given time of interest) by using compressive sensing and then perform the spectral analysis. Knowledge of ages of various groups of nodes can provide significant insights into the evolutionary process underpinning the network. It should be noted, however, that at the present the applicability of our method is limited to the networks for which information about the node age has been encoded gradually in the eigen-properties through evolution.

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References

  1. M.J. Newman, Networks, An Introduction (Oxford University Press, New York, 2010)

  2. W.-X. Wang, R. Yang, Y.-C. Lai, V. Kovanis, M.A.F. Harrison, Europhys. Lett. 94, 48006 (2011)

    Article  ADS  Google Scholar 

  3. W.-X. Wang, R. Yang, Y.-C. Lai, V. Kovanis, C. Grebogi, Phys. Rev. Lett. 106, 154101 (2011)

    Article  ADS  Google Scholar 

  4. A.-L. Barabási, R. Albert, Science 286, 509 (1999)

    Article  MathSciNet  Google Scholar 

  5. G.-M. Zhu, H.J. Yang, C.Y. Yin, B. Li, Phys. Rev. E 77, 066113 (2008)

    Article  ADS  Google Scholar 

  6. H.J. Yang, F.C. Zhao, B.H. Wang, Chaos 16 (2006)

  7. J. Ren, B. Li, Phys. Rev. E 79, 051922 (2009)

    Article  ADS  Google Scholar 

  8. J. Ren, W.-X. Wang, B. Li, Y.-C. Lai, Phys. Rev. Lett. 104, 058701 (2010)

    Article  ADS  Google Scholar 

  9. S. Jalan, G.-M. Zhu, B. Li, Phys. Rev. E 84, 046107 (2011)

    Article  ADS  Google Scholar 

  10. D.J. Watts, S.H. Strogatz, Nature 393, 440 (1998)

    Article  ADS  Google Scholar 

  11. J.G. Restrepo, E. Ott, B.R. Hunt, Phys. Rev. Lett. 93, 114101 (2004)

    Article  ADS  Google Scholar 

  12. K. Park, L. Huang, Y.-C. Lai, Phys. Rev. E 75, 026211 (2007)

    Article  MathSciNet  ADS  Google Scholar 

  13. P. Erdös, A. Rényi, Publ. Math. Inst. Hung. Acad. Sci. 5, 17 (1960)

    MATH  Google Scholar 

  14. A.V. Vazquez, A. Flammini, A. Maritan, A. Vespignani, Complexus 1, 38 (2003)

    Article  Google Scholar 

  15. A. Wagner, Mol. Biol. Evol. 18, 1283 (2001)

    Article  Google Scholar 

  16. A. Wagner, Proc. R. Soc. Lond. B Biol. Sci. 270, 457 (2003)

    Article  Google Scholar 

  17. C. von Mering, R. Krause, B. Snel, M. Cornell, S.G. Oliver, S. Fields, P. Bork, Nature 417, 399 (2002)

    Article  ADS  Google Scholar 

  18. C.R. Woese, Microbiol. Rev. 51, 221 (1987)

    Google Scholar 

  19. E. Ravasz, A.L. Somera, D.A. Mongru, Z. Oltvai, A.-L. Barabási, Science 297, 1551 (2002)

    Article  ADS  Google Scholar 

  20. E. Candès, J. Romberg, T. Tao, IEEE Trans. Inf. Theory 52, 489 (2006)

    Article  MATH  Google Scholar 

  21. E. Candès, J. Romberg, T. Tao, Commun. Pure Appl. Math. 59, 1207 (2006)

    Article  MATH  Google Scholar 

  22. E. Candès, in Proceedings of the International Congress of Mathematicians (Madrid, Spain, 2006)

  23. D. Donoho, IEEE Trans. Inf. Theory 52, 1289 (2006)

    Article  MathSciNet  Google Scholar 

  24. R.G. Baraniuk, IEEE Signal Process. Mag. 24, 118 (2007)

    Article  ADS  Google Scholar 

  25. E. Candès, M. Wakin, IEEE Signal Processing Mag. 25, 21 (2008)

    Article  ADS  Google Scholar 

  26. E. Candès, T. Tao, IEEE Trans. Inf. Theory 51, 4203 (2005)

    Article  Google Scholar 

  27. M. Girvan, M.E.J. Newman, Proc. Natl. Acad. Sci. USA 99, 7821 (2002)

    Article  MathSciNet  ADS  MATH  Google Scholar 

  28. O.E. Rössler, Phys. Lett. A 57, 397 (1976)

    Article  ADS  Google Scholar 

  29. S. Hempel, A. Koseska, J. Kurths, Z. Nikoloski, Phys. Rev. Lett. 107, 054101 (2011)

    Article  ADS  Google Scholar 

Download references

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Correspondence to G. -M. Zhu, H. J. Yang or Y. -C. Lai.

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Zhu, G.M., Yang, H.J., Yang, R. et al. Uncovering evolutionary ages of nodes in complex networks. Eur. Phys. J. B 85, 106 (2012). https://doi.org/10.1140/epjb/e2012-21019-2

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  • DOI: https://doi.org/10.1140/epjb/e2012-21019-2

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