This article provides a comprehensive survey of measurements used to characterize complex networks. It begins with an introduction to the field of complex network research, highlighting its interdisciplinary nature and the importance of understanding the topological features of networks. The article then reviews several models of complex networks, including Erdős-Rényi, Watts-Strogatz, Barabási-Albert, modular, and geographical networks. It discusses various measurements related to distance, clustering, cycles, degree distribution, and correlations, as well as methods for network classification and feature selection. The article emphasizes the importance of selecting appropriate measurements based on the specific interests and applications, and it provides a detailed analysis of the cross-correlations and trajectories of these measurements in different network models. Finally, it explores the relationship between network structure and dynamics, and the use of multivariate statistical methods for feature selection and network classification.This article provides a comprehensive survey of measurements used to characterize complex networks. It begins with an introduction to the field of complex network research, highlighting its interdisciplinary nature and the importance of understanding the topological features of networks. The article then reviews several models of complex networks, including Erdős-Rényi, Watts-Strogatz, Barabási-Albert, modular, and geographical networks. It discusses various measurements related to distance, clustering, cycles, degree distribution, and correlations, as well as methods for network classification and feature selection. The article emphasizes the importance of selecting appropriate measurements based on the specific interests and applications, and it provides a detailed analysis of the cross-correlations and trajectories of these measurements in different network models. Finally, it explores the relationship between network structure and dynamics, and the use of multivariate statistical methods for feature selection and network classification.