Structural link prediction using community information on Twitter

Abstract

Currently, social networks and social media have attracted increasing research interest. In this context, link prediction is one of the most important tasks since it can predict the existence or missing of a future relation between user members in a social network. In this paper, we describe experiments to analyze the viability of applying the within and inter cluster (WIC) measure for predicting the existence of a future link on a large-scale online social network. Compared with undirected social networks, directed social networks have received less attention and still are not well understood, mainly due to the occurrence of asymmetric links. The WIC measure combines the local structural similarity information and community information to improve link prediction accuracy. We compare the WIC measure with classical measures based on local structural similarities, using real data from Twitter, a directed and asymmetric large-scale online social network. Our experiments show that the WIC measure can be used efficiently on directed and asymmetric large-scale networks. Moreover, it outperforms all compared measures employed for link prediction.