Abstract: In this paper, a bipartite network link prediction algorithm for modularity similarity is proposed based on community structure. The limitations of bipartite networks in link prediction that lose community structure information are overcome. First, by defining the bipartite modularity and using singular value decomposition, the nodes in the network are embedded into vectors in the Euclidean space. Secondly, a framework for bipartite networks modularity similarity is proposed. The modularity similarity metrics (MS metrics) between pairs of nodes in the bipartite network are defined using vector cosine similarity. Finally, simulations are performed on three real networks based on violin graph and evaluation metrics AUC. The MS metrics are compared with nine link prediction similarity metrics. The MS metrics are used for bipartite networks link prediction with high accuracy.