We propose a visual and interactive method for discovering distinct 
groups of nodes in a network using a user-selected set of node properties 
computed from the network structure. The user's input on the visual 
separation of nodes in random 2D projections of a high-dimensional node 
property space is systematically analyzed to divide the nodes into 
distinct groups, the number of which is selected by the user interactively. 
The discovered groups are then examined to reveal their distinguishing 
characteristics. Our method is capable of discovering communities structures, 
k-partite structures, or any other structures in which the groups can be 
distinguished by a combination of node properties. We demonstrate that our 
method can effectively find and characterize a variety of group structures 
in model and real-world networks.