Link intensity prediction of online dating networks based on weighted information

The main task of the existing link prediction is to predict link existence. However, this paper proposes a new method aiming at the online dating networks, which using weighted transactional information to predict link intensity. Three different supervised learning methods are used in detail, comparing the importance of attribute features, topological features, transactional features and global features based on users´ profile information, as well as the influence of four different network graphs to the model performance. The experiment on Xiaonei dataset shows that the method used in this paper can be used to predict link intensity accurately, also illustrates that the global features have the greatest impact on the performance of model .