2004/05/18 by Gergely Palla, Illés J. Farkas, Illes Farkas +10
Business, Management and Accounting · Computer Science · Decision Sciences · Engineering · Mathematics · Physics and Astronomy · #Advanced Image Fusion Techniques #Advanced Image Processing Techniques #Artificial intelligence #Business #Channel (broadcasting) #Color image #Complex Network Analysis Techniques #Computer science #Computer vision #Demosaicing #Digital Platforms and Economics #Game Theory and Applications #Image (mathematics) #Image and Signal Denoising Methods #Image processing #Mathematics #Pattern recognition (psychology) #Programming language #Restructuring #Reverse engineering #Telecommunications #Wavelet #Wavelet transform #cond-mat.dis-nn #cond-mat.stat-mech
paper · pdf · doi:10.1103/physreve.70.046115
published as Phys. Rev. E 70, 046115 (2004) · 7 pages, 6 figures, submitted to PRE
arxiv created 2004/05/18 · openalex publication_date 2007/08/08 · arxiv updated 2009/12/01 · openalex created_date 2016/06/24 · openalex updated_date 2026/06/26
We provide a method to deduce the preferences governing the restructuring dynamics of a network from the observed rewiring of the edges. Our approach is applicable for systems in which the preferences can be formulated in terms of a single-vertex energy function with f (k) being the contribution of a node of degree k to the total energy, and the dynamics obeys the detailed balance. The method is first tested by Monte Carlo simulations of restructuring graphs with known energies; then it is used to study variations of real network systems ranging from the coauthorship network of scientific publications to the asset graphs of the New York Stock Exchange. The empirical energies obtained from the restructuring can be described by a universal function f (k) approximately -k ln k , which is consistent with and justifies the validity of the preferential attachment rule proposed for growing networks.