Crossovers in ScaleFree Networks on Geographical Space

Abstract

Complex networks are characterized by several topological properties: degree distribution, clustering coefficient, average shortest path length, etc. Using a simple model to generate scale-free networks embedded on geographical space, we analyze the relationship between topological properties of the network and attributes (fitness and location) of the vertices in the network. We find there are two crossovers for varying the scaling exponent of the fitness distribution.

4 pages, 3 figures

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• Analytical solution of stochastic model of risk-spreading with global coupling
• Factorization threshold models for scale-free networks generation