An ensemble classifier for link prediction in location based social network

Social Networks (SNs) have gained a lot of popularity on the Internet and become a hot research topic attracting many professionals from diverse areas. Recently, Location-based Social Networks (LBSNs) have attracted millions of users, experiencing a huge popularity increase over a short period of time. In Location-based social network, users can easily set their locations as a new interactive way to share with friends to inform them of their current location. In this paper, we explores the community structure of a location based social network data and propose a new link predictor for its. In the proposed approach, the network is firstly partitioned into a number of groups. Then, a supervised link predictor is learnt for each group. To do this, we have used several learning algorithms and compared their prediction accuracy to achieve the best performance. The results show that the proposed method obtains 97.88 accuracy.