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SPR-based Markov chain method for degree distributions of evolving networks

Author

Listed:
  • Zhang, Xiaojun
  • He, Zishu
  • He, Zheng
  • Rayman-Bacchus, Lez

Abstract

In this paper, we develop a stochastic process rules (SPR) based Markov chain method to calculate the degree distributions of evolving networks. This new approach overcomes two shortcomings of Shi, Chen and Liu’s use of the Markov chain method (Shi et al. 2005 [21]). In addition we show how an SPR-based Markov chain method can be effectively used to calculate degree distributions of random birth-and-death networks, which we believe to be novel. First SPR are introduced to replace traditional evolving rules (TR), making it possible to compute degree distributions in one sample space. Then the SPR-based Markov chain method is introduced and tested by using it to calculate two kinds of evolving network. Finally and most importantly, the SPR-based method is applied to the problem of calculating the degree distributions of random birth-and-death networks.

Suggested Citation

  • Zhang, Xiaojun & He, Zishu & He, Zheng & Rayman-Bacchus, Lez, 2012. "SPR-based Markov chain method for degree distributions of evolving networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(11), pages 3350-3358.
    • Handle: RePEc:eee:phsmap:v:391:y:2012:i:11:p:3350-3358
    DOI: 10.1016/j.physa.2012.01.040
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    Cited by:

    1. Wang, Difei & Jian, Lirong & Cao, Fengyuan & Xue, Chenyan, 2022. "An extended scale-free network evolution model based on star-like coupling motif embedding," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P1).

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