Temporal networks are graphs with time-varying edges, capturing the dynamic interactions between nodes over time. These networks encompass various types, including temporal networks, temporal graphs, dynamic graphs, and time-varying networks. They find applications in diverse fields, such as social network analysis, epidemiology, traffic prediction, stock market analysis, and fraud detection. Temporal network analysis techniques include visualization, algorithms for pattern identification, network embedding, time-varying graph models, and tensor decomposition for in-depth understanding and analysis.
