More and more areas use graphs for the representation of their data because it gives a connection-oriented perspective. Unfortunately, datasets are constantly growing in size, while devices have increasingly smaller screens (tablets, smartphones, etc). In order to reduce the quantity of elements displayed on screen, several techniques of information reduction can be used. Among them is graph clustering, which aggregates the elements of the original graph into clustered nodes and edges - thereby leading to a smaller graph. In this report, we present a tool for the interactive exploration and analysis of large clustered graphs. The tool empowers users to control the granularity of graphs either by direct interaction (collapsing/expanding clusters) or via a slider that automatically computes a clustered graph of the desired size. In a next step, we explore the use of learning algorithms to capture graph exploration preferences based on a history of user interactions.The learned parameters are then used to modify the action of the slider in view of mimicking the natural interaction/exploration behavior of the user.