Multi-kernel one class link prediction in heterogeneous complex networks

The heterogeneity of a network causes major challenges for link prediction in heterogeneous complex networks. To deal with this problem, supervised link prediction could be applied to integrate heterogeneous features extracted from different nodes/relations. However, supervised link prediction might be faced with highly imbalanced data issues which results in undesirable false prediction rate. In this paper, we propose a new kernel-based one-class link predictor in heterogeneous complex networks. Assuming a set of available meta-paths, a graph kernel is extracted based on each meta-path. Then, they are combined to form a single kernel function. Afterwards, one class support vector machine (OC-SVM) would be applied on the positive node pairs to train the link predictor. The proposed method has been compared with popular link predictors using DBLP network. The results show that the method outperforms other conventional link predictors in terms of prediction performances.