The amount of structured data available in our digital age is constantly growing and this calls for ways to analyze this data. One way for such analysis is through visualization, and structured data can typically be visualized as node-link graphs. Many tools are available for visualization of such node-link graphs. These tools often suffer from scalability issues, but more importantly they don't offer meaningful interaction mechanisms for the user to explore the data. This dissertation work attempts to address both of these issues, by providing and evaluating scalable and useful interaction methods for graph visualization systems. Our work mainly focuses on what we call manipulation methods, which are a set of interaction methods that enable the user to interactively manipulate the layout of a node-link graph. We provide a survey of existing interaction methods and introduce our own methods. Methods range from more specialized techniques, such as for an interactive visual recommender system, to more general procedures, which can be applied to any node-link data. We demonstrate the benefits of these interaction methods over pre-existing methods, and we also show the benefits of manipulation methods in general over visualizations that don't allow layout manipulation.