Link prediction in Sina Microblog using comprehensive features and improved SVM algorithm

Sina Microblog has become one of the most popular social networks in recent years. As a result, many interdisciplinary research directions of traditional social network have been conducted to it. But the link prediction problem in Sina Microblog has not drawn much attention till now. In this paper, we conduct a research of link prediction in Sina Microblog. According to the characteristics of Sina Microblog, we propose an effective and comprehensive feature set for link prediction in Sina Microblog. Then we apply fast classification algorithm for polynomial kernel support vector machines (FCPKSVM) to train our classifier and by transferring most of calculation from prediction phase to training phase, time complexity in prediction phase is greatly reduced. We show that a machine learning classifier trained using the proposed feature set can obtain comparable and good prediction performance for link prediction in Sina Microblog, and by introducing FCPKSVM, our method achieves far less time complexity in prediction phase compared with other classical classifiers.