  In this paper we propose weighted symmetric binary matrix factorization
(wSBMF) framework to detect overlapping communities in bipartite networks,
which describe relationships between two types of nodes. Our method improves
performance by recognizing the distinction between two types of missing
edges---ones among the nodes in each node type and the others between two node
types. Our method can also explicitly assign community membership and
distinguish outliers from overlapping nodes, as well as incorporating existing
knowledge on the network. We propose a generalized partition density for
bipartite networks as a quality function, which identifies the most appropriate
number of communities. The experimental results on both synthetic and
real-world networks demonstrate the effectiveness of our method.
