Nasrin Torab - Dept. of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran
Hadi Shakibian - Dept. of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran
Nasrollah moghadam charkari - Dept. of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran

Recently, Location-based Social Networks (LBSNs) have attracted millions of users, experiencing a hugepopularity increase over a short period of time. In Location-based social network, users can easily set their locations asa new interactive way to share with friends to inform them of their current location. Link prediction or predicting newcommunications is one of the fundamental and interesting problems in location-based social networks. In this paper, wepropose a new link predictor for LBSNs. In the proposed approach, the network is firstly partitioned into a number ofclusters. Then, a supervised link predictor is learnt for each cluster. To do this, we have used several learningalgorithms and compared their prediction accuracy to achieve the best performance. The results show that the proposedmethod obtains 97.77% accuracy

Link prediction, Location-based Social Networks (LBSNs)