We seek to enable interactive visual analytics of large-scale graphs using novel graph sampling methods and touchbased interfaces. Recently, there is a significant interest in modeling and studying real-world complex systems as large-scale graphs with numerous interconnected or interacting entities. Many real-world systems such as online social networks (OSN), world wide web (WWW) and Internet topology maps (ITMs) are very large, so capturing them in their entirety, analyzing them to extract useful information, and visualizing them for decision making are resource-consuming and challenging tasks. It is necessary to develop graph sampling approaches and integrate them with human-computer interfaces to study these large-scale graphs to understand the underlying real-world systems. The PIs will investigate sources of information loss in a graph sampling process and identify fundamental factors that need to be carefully considered in a sampling design. We also plan to develop a software system as an extension to open source libraries (igraph/networkX/boost) that employs different sampling methodologies to estimate important graph characteristics. Developing an extension to igraph/networkX/boost, instead of a standalone application, allows more seamless integration of our work with other CVDI projects. Furthermore, the project will improve interactive visual analysis of large graphs by prototyping interface methods in combination with machine analytics. We will develop multitouch and gesture techniques to provide intuitive user control of navigation, filtering, clustering, and highlighting during visual analysis. The efficiency and clarity of interfaces is critical for the success of visual analytics systems and helps users understand results and analysis processes.