  Discovery of communities in complex networks is a fundamental data analysis
problem with applications in various domains. Most of the existing approaches
have focused on discovering communities of nodes, while recent studies have
shown great advantages and utilities of the knowledge of communities of links
in networks. From this new perspective, we propose a link dynamics based
algorithm, called UELC, for identifying link communities of networks. In UELC,
the stochastic process of a link-node-link random walk is employed to unfold an
embedded bipartition structure of links in a network. The local mixing
properties of the Markov chain underlying the random walk are then utilized to
extract two emerged link communities. Further, the random walk and the
bipartitioning processes are wrapped in an iterative subdivision strategy to
recursively identify link partitions that segregate the network links into
multiple subdivisions. We evaluate the performance of the new method on
synthetic benchmarks and demonstrate its utility on real-world networks. Our
experimental results show that our method is highly effective for discovering
link communities in complex networks. As a comparison, we also extend UELC to
extracting communities of node, and show that it is effective for node
community identification.
