Correlation dimension in empirical networks.

Autor:	Moore JM; MOE Key Laboratory of Advanced Micro-Structured Materials, and School of Physics Science and Engineering, Tongji University, Shanghai 200092, People's Republic of China., Wang H; Business School, University of Shanghai for Science and Technology, Shanghai 200093, People's Republic of China., Small M; Complex Systems Group, Department of Mathematics and Statistics, University of Western Australia, Crawley 6009, Western Australia, Australia.; Mineral Resources, CSIRO, Kensington 6151, Western Australia, Australia., Yan G; MOE Key Laboratory of Advanced Micro-Structured Materials, and School of Physics Science and Engineering, Tongji University, Shanghai 200092, People's Republic of China.; Frontiers Science Center for Intelligent Autonomous Systems, Tongji University, Shanghai, 200092, People's Republic of China.; Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, People's Republic of China., Yang H; Business School, University of Shanghai for Science and Technology, Shanghai 200093, People's Republic of China., Gu C; Business School, University of Shanghai for Science and Technology, Shanghai 200093, People's Republic of China.
Jazyk:	angličtina
Zdroj:	Physical review. E [Phys Rev E] 2023 Mar; Vol. 107 (3-1), pp. 034310.
DOI:	10.1103/PhysRevE.107.034310
Abstrakt:	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 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.
Databáze:	MEDLINE
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