This survey presents a comprehensive overview of measurements used to characterize complex networks. Complex networks, which represent real-world systems, have specific topological features that influence their dynamics. The article discusses various network models, including Erdős-Rényi, Watts-Strogatz, Barabási-Albert, and community-based models. It covers key measurements such as distance, clustering, degree distribution, entropy, centrality, and community identification. The paper also addresses the analysis of network evolution, correlations between measurements, and the use of multivariate statistics for feature selection and network classification. The importance of selecting appropriate measurements for network analysis is emphasized, as well as the relationship between network structure and dynamics. The survey highlights the need for a thorough understanding of network measurements to accurately characterize and interpret complex systems. It also discusses the challenges of measuring network properties and the role of statistical methods in selecting relevant features. The paper concludes by emphasizing the significance of network measurements in understanding the behavior and properties of complex systems.This survey presents a comprehensive overview of measurements used to characterize complex networks. Complex networks, which represent real-world systems, have specific topological features that influence their dynamics. The article discusses various network models, including Erdős-Rényi, Watts-Strogatz, Barabási-Albert, and community-based models. It covers key measurements such as distance, clustering, degree distribution, entropy, centrality, and community identification. The paper also addresses the analysis of network evolution, correlations between measurements, and the use of multivariate statistics for feature selection and network classification. The importance of selecting appropriate measurements for network analysis is emphasized, as well as the relationship between network structure and dynamics. The survey highlights the need for a thorough understanding of network measurements to accurately characterize and interpret complex systems. It also discusses the challenges of measuring network properties and the role of statistical methods in selecting relevant features. The paper concludes by emphasizing the significance of network measurements in understanding the behavior and properties of complex systems.