MALP: A More Effective Meta-Paths Based Link Prediction Method in Partially Aligned Heterogeneous Social Networks

In general, online social networks include different types of nodes and edges, which means that online social networks are a type of heterogeneous information network. Link prediction is a very important research problem in heterogeneous social networks. The solution to this problem is generally to predict the possibility of a link between two nodes by extracting the characteristics of the nodes in the network. However, the information provided by a single network may not be sufficient, so useful information can be passed from other networks to assist in link prediction in the target network. This is called a partially aligned heterogeneous social network link prediction problem. In this paper, a method, called Meta-path and AUC optimization based Link Predictor(MALP), is proposed to predict the social links in the partially aligned social networks at the same time with a semi-supervised AUC optimization technology. Experimental results on real social network data show that our approach exhibits better predictive performance than other state-of-the-art methods.