Link Prediction in Heterogeneous Social Networks

TLDR: The problem of link prediction in heterogeneous networks as a multi-task, metric learning (MTML) problem is posed and the MT-SPML method is extended to account for task correlations, robustness to non-informative features and non-stationary degree distribution across networks.

Abstract: A heterogeneous social network is characterized by multiple link types which makes the task of link prediction in such networks more involved. In the last few years collective link prediction methods have been proposed for the problem of link prediction in heterogeneous networks. These methods capture the correlation between different types of links and utilize this information in the link prediction task. In this paper we pose the problem of link prediction in heterogeneous networks as a multi-task, metric learning (MTML) problem. For each link-type (relation) we learn a corresponding distance measure, which utilizes both network and node features. These link-type specific distance measures are learnt in a coupled fashion by employing the Multi-Task Structure Preserving Metric Learning (MT-SPML) setup. We further extend the MT-SPML method to account for task correlations, robustness to non-informative features and non-stationary degree distribution across networks. Experiments on the Flickr and DBLP network demonstrates the effectiveness of our proposed approach vis-a-vis competitive baselines.