Stratified Configuration Model for Simulating Networks

Abstract

Conventional network generation models exhibit limitations in capturing complex structural patternsinfluencedby nodal and link attributes. While recent models have partially addressed this issue, significant gaps persist. This work introduces an adaptation of the Configuration Model that incorporates nodal attributes as crucial factors in determining network structure. We implemented a modification in the generation process to increase the clustering coefficient and propose two distinct algorithms: one for scenarios where the complete network topology is known and another for situations of incomplete network information. We demonstrate that our methodology can replicate network topology with edge losses below 12%, results in up to a tenfold increase in the clustering coefficient, and faithfully reproduces link distributions among different nodal categories.