No abstract

We consider an entropic distance analog quantity based on the density of the Gini index in the Lorenz map, i.e., gintropy. Such a quantity might be used for pairwise mapping and ranking between various countries and regions based on income and wealth inequality. Its generalization to f-gintropy, using a function of the income or wealth value, distinguishes between regional inequalities more sensitively than the original construction.

Based on a large dataset containing thousands of real-world networks ranging from genetic, protein interaction and metabolic networks to brain, language, ecology and social networks, we search for defining structural measures of the different complex network domains (CND). We calculate 208 measures for all networks, and using a comprehensive and scrupulous workflow of statistical and machine learning methods, we investigated the limitations and possibilities of identifying the key graph measures of CNDs. Our approach managed to identify well distinguishable groups of network domains and confer their relevant features. These features turn out to be CND specific and not unique even at the level of individual CNDs. The presented methodology may be applied to other similar scenarios involving highly unbalanced and skewed datasets.