  We propose a multi-phase approach to explore network structures. In this
method, structure analysis is not carried out on the observed network directly.
Instead, certain similarity measures of the nodes are derived from the network
firstly, which are then projected onto an appropriate lower-dimensional feature
space. The clustering structure can be defined in the feature space, and
analyzed by conventional clustering algorithms. The classified data are finally
mapped back to the original network space if necessary to complete the analysis
of network structures. By mapping onto the feature space, some difficulties due
to the diversity of micro-structures and scale of the network can be
circumvented. This makes it possible for the proposed method to deal with more
general structures such as detecting groups in a random background, as well as
identifying usual community structures in networks.
