Abstract
In recent years, there has been an explosive growth in the volume of check-in data in location-based social network (LBSN). As we know, these check-in data imply temporal and spatial information about user behavior patterns. This paper proposed an overlapping community detection method based on similarity of user trajectories and reference spots similarity in LBSN. Based on traditional DBSCAN clustering algorithm, we divide the entire map into many atomic regions that contain density information. We measure the similarity of trajectories between users and combine the similarity of user’s reference spots, and then we can discover overlapping communities by edge clustering algorithm. The experimental results show that the proposed approach is more effective and more precise in discovering communities in LBSN.
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