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Abstract
Searching for well-connected user communities in a Location-based Social Network (LBSN) has been extensively investigated. However, very few studies focus on finding a group of locations in an LBSN which are significantly engaged with socially cohesive user groups. In this work, we investigate the problem of Co-engaged Location group Search (CLS) from LBSNs where the selected locations are visited frequently by the members of the socially cohesive user groups, and the locations are reachable within a given distance threshold. To the best of our knowledge, this is the first work to search for socially co-engaged location groups in LBSNs. We devise a score function to measure the co-engagement of the location groups by combining social connectivity of the cohesive user groups and check-in density of the users to the selected locations. To solve the CLS problem, we propose a Filter-and-Verify algorithm that effectively filters out ineligible locations, and their corresponding check-in users. Further, we derive a lower bound on the number of check-ins to prune the insignificant locations and develop a novel greedy forward expansion algorithm (GFA). To accelerate the computation of CLS, we propose a ranking function and devise an incremental algorithm, GIA, that can filter the unqualified location groups. We establish the effectiveness of our solutions by conducting extensive experiments on three real-world datasets.
Original language | English |
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Pages (from-to) | 2910-2926 |
Number of pages | 17 |
Journal | IEEE Transactions on Knowledge and Data Engineering |
Volume | 36 |
Issue number | 7 |
DOIs | |
Publication status | Published - Jul 2024 |
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Dive into the research topics of 'Co-Engaged Location Group Search in Location-Based Social Networks'. Together they form a unique fingerprint.Projects
- 2 Curtailed
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Towards Glass-box Deep Machine Perception for Trustworthy Artificial Intelligence
Akhtar, N. (Investigator 01)
ARC Australian Research Council
1/08/23 → 30/07/26
Project: Research
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Personalized Online Learning Analytics by Exploring Multilayer Graph Data
Li, J. (Investigator 01), Liu, C. (Investigator 02), Deng, K. (Investigator 03) & Song, T. (Investigator 04)
ARC Australian Research Council
1/01/18 → 9/10/20
Project: Research