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Local versus global knowledge in the Barabási-Albert scale-free network model
Jesús Gómez-Gardeñes1, Yamir Moreno
1Departamento de Teoría y Simulación de Sistemas Complejos, Instituto de Ciencia de Materiales de Aragón, C.S.I.C.-Universidad de Zaragoza, Zaragoza 50009, Spain.
Summary
The Barabási-Albert model
Area of Science:
- Complex Networks Analysis
- Network Science
- Statistical Mechanics
Background:
- The Barabási-Albert (BA) model is a cornerstone in complex network research.
- Its preferential attachment (PA) rule assumes global network knowledge for node connections.
Purpose of the Study:
- To investigate the impact of localized preferential attachment on complex network properties.
- To determine if global network knowledge is essential for certain BA model characteristics.
Main Methods:
- Development of a modified preferential attachment model incorporating local neighborhood information.
- Numerical simulations to analyze network properties under localized PA.
Main Results:
- Global properties like connectivity distribution and average shortest path length remain robust with local knowledge.
- Clustering coefficient and degree-degree correlations deviate from BA values, aligning more with real-world networks.
Conclusions:
- Preferential attachment does not strictly require global network awareness for key properties.
- Localized attachment models offer a more realistic approach to understanding real-world network structures and dynamics.