Quantifying long-range correlations in complex networks beyond nearest neighbors

Details Quantifying long-range correlations in complex networks beyond nearest neighbors Quantifying long-range correlations in complex networks beyond nearest neighbors

2010-05-20

arxiv.org/abs/1005.3680v1

EPL 90 (2), 28002 (2010)

Physics

Data Analysis, Statistics and Probability

Scientific paper

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 anti-correlations. The results suggest that the fluctuation exponent provides complementary information to the fractal dimension.

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