  Recent researches have discovered that rich interactions among entities in
nature and society bring about complex networks with community structures.
Although the investigation of the community structures has promoted the
development of many successful algorithms, most of them only find separated
communities, while for the vast majority of real-world networks, communities
actually overlap to some extent. Moreover, the vertices of networks can often
belong to different domains as well. Therefore, in this paper, we propose a
novel algorithm BiTector Bi-community De-tector) to efficiently mine
overlapping communities in large-scale sparse bipartite networks. It only
depends on the network topology, and does not require any priori knowledge
about the number or the original partition of the network. We apply the
algorithm to real-world data from different domains, showing that BiTector can
successfully identifies the overlapping community structures of the bipartite
networks.
