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Age-based model for weighted network with general assortative mixing

Author

Listed:
  • Zhou, Yan-Bo
  • Cai, Shi-Min
  • Wang, Wen-Xu
  • Zhou, Pei-Ling

Abstract

In this paper, we propose an evolutionary model for weighted networks by introducing an age-based mutual selection mechanism. Our model generates power-law distributions of degree, weight, and strength, which are confirmed by analytical predictions and are consistent with real observations. The investigation of the relationship between clustering and the connectivity of nodes suggests hierarchical organization in the weighted networks. Furthermore, both assortative and disassortative properties can be naturally obtained by tuning a parameter α, which controls the strength of age-based preferential attachments. Since the age information of nodes is easier to acquire than the degree and strength of nodes, and almost all empirically observed structural and weighted properties can be reproduced by the simple evolutionary regulation, our model may reveal some underlying mechanisms that are key for the evolution of weighted complex networks.

Suggested Citation

  • Zhou, Yan-Bo & Cai, Shi-Min & Wang, Wen-Xu & Zhou, Pei-Ling, 2009. "Age-based model for weighted network with general assortative mixing," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(6), pages 999-1006.
    • Handle: RePEc:eee:phsmap:v:388:y:2009:i:6:p:999-1006
    DOI: 10.1016/j.physa.2008.11.042
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    Cited by:

    1. Rui, Yikang & Ban, Yifang, 2012. "Nonlinear growth in weighted networks with neighborhood preferential attachment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(20), pages 4790-4797.
    2. Li, Pei & Yu, Jianyong & Liu, Jianxun & Zhou, Dong & Cao, Buqing, 2020. "Generating weighted social networks using multigraph," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 539(C).

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