Predicting Links in Complex Bipartite Networks Based on Strengthening Projections

Abstract

Link prediction in complex networks is a topic of high interest for many scientists that studied different areas. Many real-world complex networks have naturally bipartite structured. In this work, we proposed a link prediction method for the complex bipartite network. Recently, while link prediction in complex bipartite networks has been extensively studied, there isn't still sufficiently suitable methods for this networks. The previous works usually have some limitations. They first map the bipartite network into the unimodal projection networks. Then, they perform the link prediction using only the properties of the projection networks. However, these projection networks involve many node pairs with weak relationships when used in networks constructed from real-world data. These weak relationships lead to low probability predictions and high computation time for the algorithm. In this paper, to overcome these limitations, we first map the bipartite network into strengthening projection network model that consists of nodes that have only strong relationships. Based on this projection network, we presented the link prediction method for the complex bipartite network. We conducted extensive experiments on complex bipartite networks. The results of the experiments demonstrate that our proposed method can effectively solve the link prediction problem in complex bipartite networks.