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- W4328130847 abstract "Network correlation dimension governs the distribution of network distance in terms of a power-law model and profoundly impacts both structural properties and dynamical processes. We develop new maximum likelihood methods which allow us robustly and objectively to identify network correlation dimension and a bounded interval of distances over which the model faithfully represents structure. We also compare the traditional practice of estimating correlation dimension by modeling as a power law the fraction of nodes within a distance to a proposed alternative of modeling as a power law the fraction of nodes $mathit{at}$ a distance. In addition, we illustrate a likelihood ratio technique for comparing the correlation dimension and small-world descriptions of network structure. Improvements from our innovations are demonstrated on a diverse selection of synthetic and empirical networks. We show that the network correlation dimension model accurately captures empirical network structure over neighborhoods of substantial size and span and outperforms the alternative small-world network scaling model. Our improved methods tend to lead to higher estimates of network correlation dimension, implying that prior studies could have produced or utilized systematic underestimates of dimension." @default.
- W4328130847 created "2023-03-22" @default.
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- W4328130847 date "2023-03-21" @default.
- W4328130847 modified "2023-10-16" @default.
- W4328130847 title "Correlation dimension in empirical networks" @default.
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- W4328130847 doi "https://doi.org/10.1103/physreve.107.034310" @default.
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