  A new method for identifying soft communities in networks is proposed.
Reference nodes, either selected using a priori information about the network
or according to relevant node measurements, are obtained. Distance vectors
between each network node and the reference nodes are then used for defining a
multidimensional coordinate system representing the community structure of the
network at many different scales. For modular networks, the distribution of
nodes in this space often results in a well-separated clustered structure, with
each cluster corresponding to a community. The potential of the method is
illustrated with respect to a spatial network model and the Zachary's karate
club network.
