Dynamics of network growth and evolution: integrating non-constant edge growth, mixed attachment and reciprocity

Abstract

This work analyzes directed network evolution through a unified model that combines non-constant edge growth, mixed attachment, and reciprocity. By integrating these mechanisms, we characterize how local connection rules produce global structural patterns over time and how growth regimes alter long-term topology. The framework provides both analytical and computational tools to study temporal network behavior under realistic, non-static assumptions. These results strengthen modeling and inference for evolving real-world systems where attachment dynamics and reciprocal interactions co-exist.