Quantifying Heterogeneity of Complex Networks Based on Gintropy and Gini Indices

Characterizing the heterogeneity of complex networks is a fundamental challenge in studying complex networks. Most of the proposed measures are vertex-degree based, falling short of capturing essential characteristic features of heterogeneous networks. The Gini index is an inequality measure extensively used in economics and the social sciences and Gintropy is a newly proposed inequality measure based on the Gini index. In this paper, we use Gintropy to characterize the heterogeneity of complex networks. Our experimental results show that Gintropy can be effectively used as a heterogeneity index. The benefit of adopting Gintropy as a heterogeneity index is that it accurately quantifies the rich club graph as the most heterogeneous graph.