This chapter provides a comprehensive and systematic exploration of various theoretical models of networks, starting with the basics and building up to more advanced concepts. It begins by explaining Erdös–Rényi random networks (E-R), the building blocks of network theory. After that, it promptly advances to more complex models, such as the small-world network and the Barabási–Albert (BA) model. Each model is rigorously analyzed and formally specified, with emphasis on their unique characteristics. The chapter also offers practical guidance on estimating power-law distribution from empirical data, enhancing the applicability of these models to real-world phenomena. A standout feature of the chapter is a case study on how often scientific papers are cited, showing network theory in action. Furthermore, the chapter is enriched by practical applications using three significant software platforms, Stata, R, and Python, which allow for the tangible implementation of network theories.