Detecting overlapping communities in LBSNs by fuzzy subtractive clustering

With the increasing popularity of location-based social networks (LBSNs), community detection has emerged as an important and practical issue. One of the main shortcomings of the previous methods is that cluster’s centers have been selected randomly in clustering the communities; therefore, different results are obtained in each execution. This paper proposes an intelligent method to detect overlapping communities in LBSNs with respect to user check-ins and the attributes of venues and users. In the proposed approach, clustering is performed on the two-segment user-venue edges. The advantage of this approach is the use of subtractive clustering method, which determines cluster centers with a probability, based on the potential of each data point. Furthermore, the specified fuzzy rules have a significant role in determining the appropriate number of cluster centers. The paper concentrates on the data obtained from the Foursquare LBSN. The empirical results of the proposed approach are compared with the previous approach to clustering edges in multi-attribute and multi-mode (M²-clustering) algorithm, and a significant improvement is observed in the cost function of the community detection.