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

Abstract

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.