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Lattice scale-free networks with weighted linking.
Kongqing Yang1, Liang Huang, Lei Yang
1Institute of Applied Physics, Jimei University, Xiamen 361021, China.
Summary
This study introduces a new parameter, linking weight, to scale-free (SF) network models on lattices. This allows control over the clustering coefficient, bridging lattice SF models and SF random graphs.
Area of Science:
- Network Science
- Statistical Physics
- Complex Systems
Background:
- Scale-free (SF) networks are crucial in understanding real-world systems.
- Existing lattice-based SF network models incorporate spatial embedding effects.
- Real-world networks often exist on 2D surfaces, necessitating spatial considerations.
Purpose of the Study:
- To introduce a controllable parameter into lattice SF network models.
- To investigate the influence of this parameter on network properties.
- To bridge the gap between lattice SF models and SF random graphs.
Main Methods:
- Modification of the existing lattice SF network model.
- Introduction of a 'linking weight' parameter.
- Analysis of network properties as a function of the linking weight.
Main Results:
- The introduced linking weight parameter effectively controls the clustering coefficient.
- Network properties transition smoothly between lattice SF models and SF random graphs based on linking weight.
- The model provides a flexible framework for studying networks with varying spatial embedding and clustering.
Conclusions:
- The modified lattice SF network model offers enhanced control over network topology.
- This approach facilitates the study of diverse network structures, from regular lattices to random graphs.
- The findings are applicable to understanding real-world networks embedded in 2D spaces.