A Joint Link Prediction Method for Social Network

Abstract

The popularity of social network services has caused the rapid growth of the users. To predict the links between users has been recognized as one of the key tasks in social network analysis. Most of the present link prediction methods either analyze the topology structure of social network graph or just concern the user’s interests. These will lead to the low accuracy of prediction. Furthermore, the large amount of user interest information increases the difficulties for common interest extraction. In order to solve the above problems, this paper proposes a joint social network link prediction method-JLPM. Firstly, we give the problem formulation. Secondly, we define a joint prediction feature model(JPFM) to describe user interest topic feature and network topology structure feature synthetically, and present corresponding feature extracting algorithm. JPFM uses the LDA topic model to extract user interest topics and uses a random walk algorithm to extract the network topology features. Thirdly, by transforming the link prediction problem to a classification problem, we use the typical SVM classifier to predict the possible links. Finally, experimental results on citation data set show the feasibility of our method.