Networks represent a useful and widely adopted structure to model systems from distinct areas, such as computer science (e.g., computers linked by communication protocols), biology (e.g., chemical reactions in cells), sociology (e.g., face-to-face interactions), and others. Visual analysis of temporal networks comprises an effective way to understand the network dynamics. It facilitates the identification of patterns, anomalies, and other network properties, thus resulting in fast decision making. The amount of data in real-world networks, however, may result in a layout with high visual clutter, consequently impairing the analysis. All three network dimensions, namely node, edge, and time, play important roles in the layout construction and, consequently, in the visual analysis. This project aims to create network manipulation and visualization strategies to enhance the visual analysis of small and large temporal networks.