Predicting potential links by using strengthened projections in evolving bipartite networks

Abstract

The link prediction problem can be used for predicting the link changes that are difficult to understand and costly in network science. So far, many different models have been proposed to predict links in the network. Most of the proposed models in the link prediction have focused on the unimodal networks. In this paper, we present a link prediction model that attempts to predict links in the large-scale bipartite networks. The proposed method consists of two stages: The first stage is the extraction of the potential link set. A limitation of the previous works that addressed the link prediction problem in bipartite networks is that only the structural properties of the projected networks that were transformed from the bipartite networks are used to extract the potential links. In this paper, in addition to the structural properties of the projected networks, we take into account the structural properties of the bipartite network. Also, we employ the strengthened projection network model instead of the classical projection network models. The second stage is the calculation of prediction score of each potential link. The classical proximity measures have several disadvantages in dynamic complex networks. When network evolution that considers valuable information between node pairs are ignored, the potential information source is not efficiently used in the link prediction. Therefore, in this stage, we propose a time-aware proximity measure based on social network evolution, instead of classical proximity measures using the current state of the network. To the best of our knowledge, this is the first effort that considers the evolution in bipartite networks. To test the performance of the proposed method, we made some experiments on two real networks that have different frame size. Experimental results show that the success of our method is promising.