Prediction in signed heterogeneous networks

The problem of prediction is an important task in network analysis, which has attracted more attention from computer science communities. In this paper, prediction in signed heterogeneous networks is addressed, which contains two aspects, link prediction and sign prediction. Most of previous studies focus on non-signed networks that have only positive links or homogeneous networks that have only one type of nodes. However, there are many signed heterogeneous networks in which the nodes and links belong to different types and links can be either positive (indicating relationships such as trust, preferences, friendship, and etc) or negative (indicating relationships such as distrust, dislike, opposition, and etc) in real world. For link prediction, a rule-based methodology called RulePredict is proposed in the paper. In RulePredict, we first extract all features systematically which contain positive features that promote the existence of links and negative ones that reduce the possibility reversely. Then, the weights associated with different features will be learned by a supervised method based on generalized least squares (GLS). For sign prediction, we put forward a new method called HeteSign to calculate the polarity of the links based on the similarity of two objects depends on their linked objects in heterogeneous networks. Experiments are conducted on real networks, the IMDB and Epinions networks, which demonstrate that our approach gets better performance in terms of accuracy.