A comprehensive study on the effects of using data mining techniques to predict tie strength

In this paper, the problem of tie strength prediction is modeled as a data mining problem on which different supervised and unsupervised mining methods are applicable. We propose a comprehensive study on the effects of using different classification techniques such as decision trees, Naive Bayes and so on; in addition to some ensemble classification methods such as Bagging and Boosting methods for predicting tie strength of users of a social network. LinkedIn social network is used as a real case study and our experimental results are proposed on its extracted data. Several models, based on basic techniques and ensemble methods are created and their efficiencies are compared based on F-Measure, accuracy, and average executing time. Our experimental results show that, our profile-behavioral based model has much better accuracy in comparison with profile-data based models techniques.