  To understand the formation, evolution, and function of complex systems, it
is crucial to understand the internal organization of their interaction
networks. Partly due to the impossibility of visualizing large complex
networks, resolving network structure remains a challenging problem. Here we
overcome this difficulty by combining the visual pattern recognition ability of
humans with the high processing speed of computers to develop an exploratory
method for discovering groups of nodes characterized by common network
properties, including but not limited to communities of densely connected
nodes. Without any prior information about the nature of the groups, the method
simultaneously identifies the number of groups, the group assignment, and the
properties that define these groups. The results of applying our method to real
networks suggest the possibility that most group structures lurk undiscovered
in the fast-growing inventory of social, biological, and technological networks
of scientific interest.
