Crossovers in ScaleFree Networks on Geographical Space
Disordered Systems and Neural Networks
2013-11-20 v1 Statistical Mechanics
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.
Keywords
Cite
@article{arxiv.cond-mat/0512639,
title = {Crossovers in ScaleFree Networks on Geographical Space},
author = {Satoru Morita},
journal= {arXiv preprint arXiv:cond-mat/0512639},
year = {2013}
}
Comments
4 pages, 3 figures