Exploiting user-to-user topic inclusion degree for link prediction in social-information networks

As one kind of typical network big data, social-information networks (such as Weibo and Twitter) include both the complex network structure among users and the rich microblog/tweets information published by users. Understanding the interplay of rich content and social relationships is potentially valuable to the fundamental network mining task, i.e. the link prediction. Although some of the link prediction methods have been proposed by combining topological and non-topological information simultaneously, the in-depth analysis of the rich content still being in a minority, and the rich content in the social-information networks is still underused in solving link prediction. In this paper, we approach the link prediction problem in social-information network by combining network structure and topic information which is extracted from users' rich content. We first define a kind of user-to-user topic inclusion degree (TID) based on the dissemination mechanism of the published content in the social-information networks, and then construct a TID-based sparse network. On the basis, we build a fusion probabilistic matrix factorization model which solves the link prediction problem by fusing the information of the original following/followed network and the TID-based network in a unified probabilistic matrix factorization framework. We conduct link prediction experiments on two types of real social-information network datasets, i.e. Twitter and Weibo. The experimental results demonstrate that the proposed method is more effective in solving the link prediction problem in social-information networks.