Interaction is a vital component in the visualization of multivariate networks. By allowing people to browse data sets with interactions like panning and zoom- ing, it enables much more information to be seen and explored than would oth- erwise be possible with static visualization. Overview-based interactions afford the user the ability to understand a complete picture of the data or informa- tion landscape and to decide where to direct her attention. Through search and filtering, interaction can reduce cognitive effort on users by allowing them to locate, focus on and understand subsets of the data in isolation. Pivoting and other navigational interactions at both the view- and data-level allow people to identify and then to transition between areas of interest. While there are methods for interacting with graphs and dimensions sep- arately, the combination of both needs special attention. The challenge is to clearly visualize multiple sets of individual dimensions as well as to offer a useful visual overview of data, and allow transitions between these to be easily under- stood. Moreover, we need to find ways to support users in navigating through the complex data space (graphs x dimensions) without "getting lost" without an overburden of interaction actions, as this might me frustrating for the user.