A semi-supervised social relationships inferred model based on mobile phone data

Exploring the relationships of humans is an important study in the mobile communication network. But the relationship prediction accuracy is not good enough when the number of known relationship labels (e.g., “friend” and “colleague”) is small, especially when the number of different relation classes are imbalanced in the mobile communication network. To deal with issues, we present a semi-supervised social relationships inferred model. This model can infer the relationships based on a large amount of unlabeled data or a small amount of labeled data. The model is a co-training style semi-supervised model which is combined with the support vector machine and naive Bayes. The final relationship labels are decided by the two classifiers. The proposed model is evaluated by a real mobile communication network dataset and the experiment results show that the model is effective in relationship mining, especially when the relationship network is in a stable state.