This review discusses the structure and function of complex networks, focusing on their properties, models, and processes. It begins by introducing the concept of networks as sets of vertices connected by edges, and highlights their prevalence in various real-world systems such as social, information, technological, and biological networks. The review covers key concepts like the small-world effect, degree distributions, clustering, network correlations, and random graph models. It also explores models of network growth, such as preferential attachment, and processes that occur on networks, including percolation, epidemiological models, and search algorithms. The review emphasizes the importance of understanding network structure to predict and analyze the behavior of complex systems. It discusses the differences between random graphs and real-world networks, noting that real networks often exhibit non-random properties such as clustering and short path lengths. The review also addresses the challenges of analyzing large networks and the development of statistical methods to quantify their properties. It concludes by highlighting the importance of further research in understanding network structure and function.This review discusses the structure and function of complex networks, focusing on their properties, models, and processes. It begins by introducing the concept of networks as sets of vertices connected by edges, and highlights their prevalence in various real-world systems such as social, information, technological, and biological networks. The review covers key concepts like the small-world effect, degree distributions, clustering, network correlations, and random graph models. It also explores models of network growth, such as preferential attachment, and processes that occur on networks, including percolation, epidemiological models, and search algorithms. The review emphasizes the importance of understanding network structure to predict and analyze the behavior of complex systems. It discusses the differences between random graphs and real-world networks, noting that real networks often exhibit non-random properties such as clustering and short path lengths. The review also addresses the challenges of analyzing large networks and the development of statistical methods to quantify their properties. It concludes by highlighting the importance of further research in understanding network structure and function.