Discrete Ricci Flow: A powerful method for community detection in location-based social networks

Abstract

Community detection is crucial to understanding behavioral patterns in location-based social networks (LBSNs) where user locations, media, and check-ins are involved. This hierarchical structure enables the formation of user communities, where a community represents a group of users sharing similar interests. In addition, selecting an appropriate community for a recommendation scenario is crucial and challenging. To address these issues, in this article, we propose a novel method to link LBSNs to the Discrete Ricci Flow (DRF) community detection algorithm. Then we use the communities formed by the Ricci curvature of the network to provide recommendations in a user-based collaborative filtering (CF) recommender system. Our evaluation method considers spatial–temporal features and user relationships. The evaluation encompasses unsupervised and supervised learning methodologies, employing the modularity evaluation index and the CF recommender system. Comparative analysis against traditional community detection algorithms, including Leiden, Infomap, Walktrap, and Fast Greedy, reveals the superior performance of our proposed method, as it achieves an impressive 0.5075% and 0.8486% modularity scores for Gowalla and Brightkite respectively that indicates the efficacy of the method in capturing the inherent structure of the data. Furthermore, when integrated into the CF recommender system, the proposed method based on DRF demonstrates superior performance compared to other community detection methods for different data sets such as Gowalla and Brightkite. In particular, for Gowalla it improves the performance of the Point Of Interest (POI) recommendation system by an average of 10.92% and 8.02% in Recall@15 and Recall@20, respectively.