Collaborative linear manifold learning for link prediction in heterogeneous networks

Abstract

Link prediction in heterogeneous networks aims at predicting missing interactions between pairs of nodes with the help of the topology of the target network and interconnected auxiliary networks. It has attracted considerable attentions from both computer science and bioinformatics communities in the recent years. In this paper, we introduce a novel Collaborative Linear Manifold Learning (CLML) algorithm. It can optimize the consistency of nodes similarities by collaboratively using the manifolds embedded between the target network and the auxiliary network. The experiments on four benchmark datasets have demonstrated the outstanding advantages of CLML, not only in the high prediction performance compared to baseline methods, but also in the capability to predict the unknown interactions in the target networks accurately and effectively.