Volume 90, Number 2, April 2010
|Number of page(s)||6|
|Section||Interdisciplinary Physics and Related Areas of Science and Technology|
|Published online||13 May 2010|
Quantifying long-range correlations in complex networks beyond nearest neighbors
Potsdam Institute for Climate Impact Research - 14412 Potsdam, Germany, EU
2 Levich Institute, City College of New York - New York, NY 10031, USA
3 Division of Natural Sciences, College of Mount Saint Vincent - Riverdale, NY 10471, USA
Corresponding author: firstname.lastname@example.org
Accepted: 9 April 2010
We propose a fluctuation analysis to quantify spatial correlations in complex networks. The approach considers the sequences of degrees along shortest paths in the networks and quantifies the fluctuations in analogy to time series. In this work, the Barabasi-Albert (BA) model, the Cayley tree at the percolation transition, a fractal network model, and examples of real-world networks are studied. While the fluctuation functions for the BA model show exponential decay, in the case of the Cayley tree and the fractal network model the fluctuation functions display a power law behavior. The fractal network model comprises long-range anticorrelations. The results suggest that the fluctuation exponent provides complementary information to the fractal dimension.
PACS: 89.75.Fb – Structures and organization in complex systems / 05.40.-a – Fluctuation phenomena, random processes, noise, and Brownian motion / 05.45.Df – Fractals
© EPLA, 2010
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