In this paper we introduce a graph clustering method based on
dense bipartite subgraph mining. The method applies a mixed
graph model (both standard and bipartite) in a three-phase
algorithm. First a seed mining method is applied to find seeds
of clusters, the second phase consists of refining the seeds,
and in the third phase vertices outside the seeds are clustered.
The method is able to detect overlapping clusters, can handle
outliers and applicable without restrictions on the degrees of
vertices or the size of the clusters. The running time of the
method is polynomial. A theoretical result is introduced on
density bounds of bipartite subgraphs with size and local
density conditions. Test results on artificial datasets and
social interaction graphs are also presented
