  Many real-world networks display a natural bipartite structure. It is
necessary and important to study the bipartite networks by using the bipartite
structure of the data. Here we propose a modification of the clustering
coefficient given by the fraction of cycles with size four in bipartite
networks. Then we compare the two definitions in a special graph, and the
results show that the modification one is better to character the network. Next
we define a edge-clustering coefficient of bipartite networks to detect the
community structure in original bipartite networks.
