Temporal networks are a framework for modeling systems where the timing of interactions is crucial. These systems include communication networks, biological systems, and infrastructural networks, where edges (interactions) are not continuously active but occur at specific times. Unlike static networks, temporal networks consider the time dimension, allowing for a more accurate representation of dynamics such as disease spread or information diffusion. The study of temporal networks is interdisciplinary, with applications ranging from social networks to ecological systems. Key concepts include time-respecting paths, reachability, and the impact of temporal structure on network dynamics. Measures such as latency, average latency, and connectivity are used to analyze temporal networks, while methods like contact sequences and interval graphs help represent temporal data. Temporal networks are particularly useful in scenarios where the timing of interactions significantly affects outcomes, such as in disease control or information spreading. The review discusses various types of temporal networks, including communication, physical proximity, and biological systems, and highlights the importance of considering temporal aspects in network analysis. The study emphasizes the need for specialized methods to account for the dynamic nature of temporal networks, distinguishing them from static network models.Temporal networks are a framework for modeling systems where the timing of interactions is crucial. These systems include communication networks, biological systems, and infrastructural networks, where edges (interactions) are not continuously active but occur at specific times. Unlike static networks, temporal networks consider the time dimension, allowing for a more accurate representation of dynamics such as disease spread or information diffusion. The study of temporal networks is interdisciplinary, with applications ranging from social networks to ecological systems. Key concepts include time-respecting paths, reachability, and the impact of temporal structure on network dynamics. Measures such as latency, average latency, and connectivity are used to analyze temporal networks, while methods like contact sequences and interval graphs help represent temporal data. Temporal networks are particularly useful in scenarios where the timing of interactions significantly affects outcomes, such as in disease control or information spreading. The review discusses various types of temporal networks, including communication, physical proximity, and biological systems, and highlights the importance of considering temporal aspects in network analysis. The study emphasizes the need for specialized methods to account for the dynamic nature of temporal networks, distinguishing them from static network models.