2025/05/15 by Mattia Marzi, Marzi, Mattia, Francesca Giuffrida +5 · 2 citations
Computer Science · Economics, Econometrics and Finance · Physics and Astronomy · #Data Analysis #FOS: Computer and information sciences #FOS: Economics and business #FOS: Physical sciences #Physics and Society (physics.soc-ph) #Social and Information Networks (cs.SI) #Statistical Finance (q-fin.ST) #Statistics and Probability (physics.data-an) #cs.SI #physics.data-an #physics.soc-ph #q-fin.ST
paper · pdf · doi:10.48550/arxiv.2505.10373
published as Phys. Rev. Research 8 (013141) (2026) · 20 pages, 10 figures - Python code available at: https://github.com/mattiamarzi/fit2SM
arxiv created 2026/07/29 · arxiv updated 2026/07/30
The study of probabilistic models for the analysis of complex networks represents a flourishing research field. Among the former, Exponential Random Graphs (ERGs) have gained increasing attention over the years. So far, only linear ERGs have been extensively employed to gain insight into the structural organisation of real-world complex networks. None, however, is capable of accounting for the variance of the empirical degree distribution. To this aim, non-linear ERGs must be considered. After showing that the usual mean-field approximation forces the degree-corrected version of the two-star model to degenerate, we define a fitness-induced variant of it. Such a `softened' model is capable of reproducing the sample variance, while retaining the explanatory power of its linear counterpart, within a purely canonical framework.