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Scaling of weighted spectral distribution in deterministic scale-free networks

Author

Listed:
  • Jiao, Bo
  • Nie, Yuan-ping
  • Shi, Jian-mai
  • Huang, Cheng-dong
  • Zhou, Ying
  • Du, Jing
  • Guo, Rong-hua
  • Tao, Ye-rong

Abstract

Scale-free networks are abundant in the real world. In this paper, we investigate the scaling properties of the weighted spectral distribution in several deterministic and stochastic models of evolving scale-free networks. First, we construct a new deterministic scale-free model whose node degrees have a unified format. Using graph structure features, we derive a precise formula for the spectral metric in this model. This formula verifies that the spectral metric grows sublinearly as network size (i.e., the number of nodes) grows. Additionally, the mathematical reasoning of the precise formula theoretically provides detailed explanations for this scaling property. Finally, we validate the scaling properties of the spectral metric using some stochastic models. The experimental results show that this scaling property can be retained regardless of local world, node deleting and assortativity adjustment.

Suggested Citation

  • Jiao, Bo & Nie, Yuan-ping & Shi, Jian-mai & Huang, Cheng-dong & Zhou, Ying & Du, Jing & Guo, Rong-hua & Tao, Ye-rong, 2016. "Scaling of weighted spectral distribution in deterministic scale-free networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 451(C), pages 632-645.
    • Handle: RePEc:eee:phsmap:v:451:y:2016:i:c:p:632-645
    DOI: 10.1016/j.physa.2016.01.096
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    References listed on IDEAS

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