The chapter "The Structure and Function of Complex Networks" by M. E. J. Newman reviews the development of techniques and models used to understand and predict the behavior of complex networks, inspired by empirical studies of systems like the Internet, social networks, and biological networks. The review covers various concepts such as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes on networks. The introduction highlights the importance of networks in various fields, including mathematics, social sciences, and computer science. It discusses the shift from analyzing small graphs to studying large-scale statistical properties of networks, driven by advancements in data collection and computational power. The chapter also emphasizes the need for statistical methods to analyze large networks, as visual inspection is impractical. The content is organized into several sections, including an overview of real-world networks, common properties of networks, and detailed discussions on mathematical modeling and dynamical processes. The chapter provides a comprehensive review of the field, covering both theoretical developments and empirical studies, and points to future research directions.The chapter "The Structure and Function of Complex Networks" by M. E. J. Newman reviews the development of techniques and models used to understand and predict the behavior of complex networks, inspired by empirical studies of systems like the Internet, social networks, and biological networks. The review covers various concepts such as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes on networks. The introduction highlights the importance of networks in various fields, including mathematics, social sciences, and computer science. It discusses the shift from analyzing small graphs to studying large-scale statistical properties of networks, driven by advancements in data collection and computational power. The chapter also emphasizes the need for statistical methods to analyze large networks, as visual inspection is impractical. The content is organized into several sections, including an overview of real-world networks, common properties of networks, and detailed discussions on mathematical modeling and dynamical processes. The chapter provides a comprehensive review of the field, covering both theoretical developments and empirical studies, and points to future research directions.