This chapter presents a unified framework for analyzing complex networks through statistical models. Starting with the Inhomogeneous Erdős-Rényi model’s concept of independent edge probabilities, we progress through increasingly sophisticated representations, including the Erdös-Rényi, Stochastic Block Model, and Random Dot Product Graph (RDPG) models. We explore how each model generalizes its predecessors, with the RDPG encompassing many earlier models under certain conditions. The crucial role of positive semidefiniteness in connecting block models to RDPGs is examined, providing insight into model interrelationships. We also introduce models addressing specific network characteristics, such as heterogeneous node degrees and edge-based clustering. The chapter extends to multiple and correlated network models, demonstrating how concepts from simpler models inform more complex scenarios. A hierarchical framework is presented, unifying these models and illustrating their relative generality, thus laying the groundwork for advanced network analysis techniques.