Link Prediction in Directed Networks Utilizing the Role of Reciprocal Links

Link prediction in directed networks has always been a hot topic in many fields including network science, information system and data mining. Intuitively, once links are endowed with certain orientations, their reciprocate nature can potentially provide extra information for guiding link prediction. However, the role of reciprocal links in the formation of directed closure triads and their ability to enhance link prediction accuracy are not thoroughly investigated yet in existing works. In this paper, we first design an empirical test to investigate the role of reciprocal links in different types of directed networks by taking advantage of null models. Subsequently, based on solid evidence of the empirical test, two novel weighting mechanisms for link prediction are proposed utilizing reciprocity as extra information. The performance of proposed methods is comprehensively studied on eight realistic networks compared with several groups of benchmarks. Experimental results indicate that the proposed methods are more effective and robust than two state-of-the-art weighting methods and eight well-performing similarity indices.