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Growing scale-free networks with tunable distributions of triad motifs

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  • Li, Shuguang
  • Yuan, Jianping
  • Shi, Yong
  • Zagal, Juan Cristóbal

Abstract

Network motifs are local structural patterns and elementary functional units of complex networks in real world, which can have significant impacts on the global behavior of these systems. Many models are able to reproduce complex networks mimicking a series of global features of real systems, however the local features such as motifs in real networks have not been well represented. We propose a model to grow scale-free networks with tunable motif distributions through a combined operation of preferential attachment and triad motif seeding steps. Numerical experiments show that the constructed networks have adjustable distributions of the local triad motifs, meanwhile preserving the global features of power-law distributions of node degree, short average path lengths of nodes, and highly clustered structures.

Suggested Citation

  • Li, Shuguang & Yuan, Jianping & Shi, Yong & Zagal, Juan Cristóbal, 2015. "Growing scale-free networks with tunable distributions of triad motifs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 428(C), pages 103-110.
    • Handle: RePEc:eee:phsmap:v:428:y:2015:i:c:p:103-110
    DOI: 10.1016/j.physa.2015.02.012
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    References listed on IDEAS

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    1. Barabási, Albert-László & Albert, Réka & Jeong, Hawoong, 1999. "Mean-field theory for scale-free random networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 272(1), pages 173-187.
    2. Zhang, Zhongzhi & Rong, Lili & Wang, Bing & Zhou, Shuigeng & Guan, Jihong, 2007. "Local-world evolving networks with tunable clustering," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 380(C), pages 639-650.
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    Cited by:

    1. Wang, Jun & Zhang, Qian-Ming & Zhou, Tao, 2019. "Tag-aware link prediction algorithm in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 105-111.
    2. Pi, Xiaochen & Tang, Longkun & Chen, Xiangzhong, 2021. "A directed weighted scale-free network model with an adaptive evolution mechanism," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 572(C).

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