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Updated: Jul 3, 2026
09:49
Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks
Published on: September 25, 2021
Evolution of weighted scale-free networks in empirical data
1Department of Physics, Korea Advanced Institute of Science and Technology, 305-701 Daejon, Korea.
Summary
This study reveals that nonlinear relationships in weighted scale-free networks emerge from nonlocal edge creation and weight dynamics. Degree-driven preferential attachment better explains real-world network evolution than strength-driven models.
Area of Science:
- Network Science
- Complex Systems
- Statistical Physics
Background:
- Weighted scale-free networks display both linear and nonlinear degree-strength relationships.
- Existing theoretical models for these relationships lack empirical validation.
Purpose of the Study:
- To test theoretical models of weighted scale-free network evolution against empirical data.
- To elucidate the mechanisms driving linear and nonlinear degree-strength relationships.
Main Methods:
- Collected temporal data from online bulletin boards and a movie actor network.
- Measured vertex degree and strength growth rates, and edge weight dynamics.
- Constructed linear and nonlinear growth models based on empirical rates within a preferential attachment framework.
Main Results:
- Nonlinear degree-strength relationships necessitate nonlocal edge creation and weight dynamics.
- Degree-driven preferential attachment (PA) is more representative of real systems than strength-driven PA.
- Empirical data supports the necessity of specific dynamic processes for nonlinear network evolution.
Conclusions:
- The study validates theoretical models by demonstrating the importance of nonlocal dynamics in reproducing nonlinear degree-strength relationships.
- Findings suggest that degree-driven preferential attachment is a more accurate descriptor for real-world weighted scale-free networks.
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