Community Detection from Bipartite Networks

Community detection from networks is one of the important and challenging research topics of social network analysis, especially from bipartite networks. In this paper, we propose an algorithm for detecting communities from bipartite networks based on ant colony optimization. Such an algorithm allows many-to-many correspondence between different types of communities. Experimental results demonstrate that our algorithm can extract multi-facet communities from bipartite networks and obtain high quality of partitioning communities.