Improving the Link Prediction by Exploiting the Collaborative and Context-Aware Social Influence

The study of link prediction has attracted increasing attention with the booming social networks. Researchers utilized topological features of networks and the attribute features of nodes to predict new links in the future or find the missing links in the current network. Some of the works take topic into consideration, but they don't think of the social influence that has potential impacts on link prediction. Hence, it leads us to introduce social influence into topics to find contexts. In this paper, we propose a novel model under the collaborative filter framework and improve the link prediction by exploiting context-aware social influence. We also adopt the clustering algorithm with the use of topological features, thus we incorporate the social influence, topic and topological structure to improve the quality of link prediction. We test our method on Digg data set and the results of the experiment demonstrate that our method performs better than the traditional approaches.