LAUSR

Abstract It is well known that the number of users is increasing rapidly in online social networks. People are linked through various types of social relationships. Detecting the type of social relationships is fundamental to improve performance on many applications in social networks. Existing studies mainly focus on predicting social relationships in the same network based on its own abundant information. However, there are few works on predicting social relationships across different networks. In this paper, we study a tri-training model to predict the labels of social relationships across different networks based on BP neural networks. Firstly, we aim to obtain the hidden common characteristics between two different networks. Depending on these hidden common characteristics, unlabeled target samples are assigned pseudo labels by training two BP neural networks. Then we capture the special features for the target network by analyzing the structural characters. Finally, combining with the special and common features, we optimize the third BP neural network by training the pseudo labeled target samples. After training, we can use the third optimized BP neural network to predict the types of social relationships in the target network. We evaluate the model and compare it with some existing methods on six real online social networks. The experimental results indicate that our model can effectively exploit the unlabeled target sample and outperform the existing methods on multiple metrics, and also show that the special features in the target network can help to enhance the learning performance.