Reproducing the first and second moments of empirical degree distributions
This work provides a novel method for modeling degree distributions in complex networks, addressing a known limitation of linear ERGs for network scientists.
The authors address the inability of linear Exponential Random Graphs (ERGs) to reproduce the variance of empirical degree distributions. They introduce a fitness-induced variant of the degree-corrected two-star model that successfully reproduces sample variance while maintaining explanatory power.
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.