The chapter introduces the concept of temporal networks, which are graphs where the edges (connections between vertices) are active at specific times. Unlike traditional static networks, temporal networks capture the temporal structure of interactions, which can significantly affect the dynamics of systems such as disease spread or information diffusion. The authors discuss various types of temporal networks, including person-to-person communication, one-to-many information dissemination, physical proximity, cell biology, distributed computing, infrastructural networks, neural and brain networks, and ecological networks. They highlight the importance of considering the temporal dimension in network analysis, especially when the dynamics of the system under study are influenced by the timing and sequence of interactions. The chapter also covers measures for characterizing the temporal-topological structure of temporal networks, such as time-respecting paths, reachability, connectivity, distances, latencies, and centrality measures. These measures help in understanding how information or processes propagate through the network over time. Additionally, the authors discuss methods for representing temporal data as static graphs and models for temporal networks, including temporal exponential random graphs and randomized reference models. Finally, the chapter explores the application of temporal network analysis to spreading dynamics and compartmental models, emphasizing the impact of bursty event dynamics and temporal inhomogeneities on the behavior of dynamical systems. The authors conclude by discussing the interdisciplinary nature of the field and the need for interdisciplinary collaboration to advance the understanding and modeling of temporal networks.The chapter introduces the concept of temporal networks, which are graphs where the edges (connections between vertices) are active at specific times. Unlike traditional static networks, temporal networks capture the temporal structure of interactions, which can significantly affect the dynamics of systems such as disease spread or information diffusion. The authors discuss various types of temporal networks, including person-to-person communication, one-to-many information dissemination, physical proximity, cell biology, distributed computing, infrastructural networks, neural and brain networks, and ecological networks. They highlight the importance of considering the temporal dimension in network analysis, especially when the dynamics of the system under study are influenced by the timing and sequence of interactions. The chapter also covers measures for characterizing the temporal-topological structure of temporal networks, such as time-respecting paths, reachability, connectivity, distances, latencies, and centrality measures. These measures help in understanding how information or processes propagate through the network over time. Additionally, the authors discuss methods for representing temporal data as static graphs and models for temporal networks, including temporal exponential random graphs and randomized reference models. Finally, the chapter explores the application of temporal network analysis to spreading dynamics and compartmental models, emphasizing the impact of bursty event dynamics and temporal inhomogeneities on the behavior of dynamical systems. The authors conclude by discussing the interdisciplinary nature of the field and the need for interdisciplinary collaboration to advance the understanding and modeling of temporal networks.