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 frac
tion 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.
