Mining Missing Links in Directed Social Networks based on Significant Motifs

Abstract

Link prediction in directed social networks is a challenging and promising problem in both communication networks and data mining. Most existing methods of mining missing directed links are based on structural similarity and the inner contributions of neighborhood nodes are usually ignored. In this paper, taking node attributes into consideration, the potential value of each node is deduced based on a value transfer function. Combing the effects of two significant network motifs, a potential value index (PVI) for link prediction is proposed. PVI can utilize the in-depth information of surrounding environments. It also reflects the motivation of link formation in directed social networks. Experimental results on eight real-world social networks show that PVI outperforms eight state-of-the-art indices with only a quasi-local complexity. It can be well applied in large scale networks.