A pattern based supervised link prediction in directed complex networks

Abstract

Link prediction is one of the most interesting tasks in complex network analysis. Numerous recently published link prediction methods have focused on utilizing network models close to real networks to improve performance of link prediction. Directed, temporal, weighted and heterogeneous network models are some examples of the favored network models. Most published link prediction metrics cannot take into account the effect of links directions on link formation. In this study, we propose a pattern based supervised link prediction approach to improve link prediction accuracy of Triad Closeness (TC) metric in directed complex networks. The proposed pattern based link prediction metric is compared with TC metric and the state-of-the-art link prediction metrics to evaluate the effectiveness of the proposed metric. Experimental results in two citation networks show that the proposed metric improves remarkably link prediction accuracy of TC metric and obtains the highest link prediction performance compared to the state-of-the-art link prediction metrics.