Communities in social networks may overlap, with some hub nodes belonging to multiple communities. They may also have outliers, which are nodes that belong to no community. The criterion to locate hubs or outliers is network dependent. Previous methods usually require this information as input parameters, e.g., an expected number of communities, with no intuition or assistance. Here we present a visual data mining approach, which first helps the user to make appropriate parameter selections by observing initial data visualizations, and then finds and extracts overlapping community structures from the network. Experimental results verify the scalability and accuracy of our approach on real network data and show its advantages over previous methods.