While there are many well-established techniques to analyze and visualize static social networks, visual analysis of dynamic (i.e., time-oriented) network data emerged in recent years as a relevant research topic, facing several open problems. The dynamic nature of this kind of data, indeed, poses the challenge of understanding both its relational aspect (the structure of social interactions) and its temporal aspect (how they change over time). In this doctoral work, we investigate how a visual analytics approach, integrating automatic analysis, visualization, and user interaction techniques, can support the examination of such dynamic networks. In particular, by focusing on this research problem, we present the following contributions: 1. we propose a set of novel metrics (change centrality metrics) to specifically analyse how the network structure changes over time; 2. we combine different visual encodings for the time-oriented aspect of network data, enabling smooth transformations between different views; 3. we introduce novel techniques for user interaction, such as interactive control of dynamic layout stability and the vertigo zoom, allowing seamless transitions between relational and temporal perspectives on dynamic network data. We illustrate our approach by describing a prototypical implementation and demonstrate its utility by introducing a real-world usage scenario. Furthermore, we provide a validation of our approach by reporting findings from expert reviews (involving experts from both the visualization community and the problem domain) as well as from two task-based user-studies, namely a qualitative evaluation and a quantitative controlled experiment. These findings afford an indication of the overall validity of our approach and allow us to discuss how particular techniques and their combinations can support specific analytical tasks on dynamic network data.