2016/03/31 by Steffen Karalus, Joachim Krug
Economics, Econometrics and Finance · Mathematics · Physics and Astronomy · #Cluster analysis #Combinatorics #Complex Network Analysis Techniques #Complex Systems and Time Series Analysis #Complex network #Degree (music) #Degree distribution #Distribution (mathematics) #Eigenvalues and eigenvectors #Geometry #Laplace operator #Mathematical analysis #Mathematics #Opinion Dynamics and Social Influence #Physics #Point (geometry) #Power law #Range (aeronautics) #Scale-free network #Scaling #Spectral clustering #Statistical physics #Statistics #Topology (electrical circuits) #cond-mat.stat-mech #physics.soc-ph
paper · pdf · doi:10.1103/physreve.93.062306
published as Phys. Rev. E 93, 062306 (2016) · 9 pages, 8 figures
arxiv created 2016/05/24 · openalex publication_date 2016/06/10 · arxiv updated 2016/06/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
We study the importance of local structural properties in networks which have been evolved for a power-law scaling in their Laplacian spectrum. To this end, the degree distribution, two-point degree correlations, and degree-dependent clustering are extracted from the evolved networks and used to construct random networks with the prescribed distributions. In the analysis of these reconstructed networks it turns out that the degree distribution alone is not sufficient to generate the spectral scaling and the degree-dependent clustering has only an indirect influence. The two-point correlations are found to be the dominant characteristic for the power-law scaling over a broader eigenvalue range.