Abstract
Community search is a fundamental problem in analyzing and managing graph data, which is searching for an optimal community based on query nodes. Attribute community search and geosocial community search have been extensively investigated, however, very few studies consider location-based social networks with attributes where users can get desired locations for some specified attributes. In this paper, we propose the Attribute Geosocial Community Search problem (AGCS) in a location-based social network with attributes, which aims to find a user community and a cluster of spatial locations with attributes that are densely connected simultaneously, while the community and the cluster should have high community metric based on attributes and check-in information. The AGCS can be used in many graph analysis applications, such as user and location recommendation, and geosocial data analysis. To solve this problem, we first defined a novel community metric based on attribute constraints and check-in information. Then, we explore three novel search algorithms: a basic algorithm based on k-core, a global algorithm for greedy deletion, and an improved global algorithm. Finally, we conduct comprehensive experimental studies, which demonstrate that our proposed solutions can efficiently searching user community and attribute location cluster in location-based social networks.
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Acknowledgements
This work is partially supported by the National Natural Science Foundation of China (Nos. 62002245, 62102271, 61802268), Natural Science Foundation of Liaoning Province (Nos. 2022-MS-303, 2022-BS-218).
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An, Y., Zong, C., Li, R., Qiu, T., Zhang, A., Zhu, R. (2023). Searching User Community and Attribute Location Cluster in Location-Based Social Networks. In: Yang, X., et al. Advanced Data Mining and Applications. ADMA 2023. Lecture Notes in Computer Science(), vol 14180. Springer, Cham. https://doi.org/10.1007/978-3-031-46677-9_27
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DOI: https://doi.org/10.1007/978-3-031-46677-9_27
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