Densely Connected User Community and Location Cluster Search in Location-Based Social Networks
J Kim, T Guo, K Feng, G Cong, A Khan, FM Choudhury
Proceedings of the ACM SIGMOD International Conference on Management of Data | ACM | Published : 2020
Searching for a community based on query nodes in a graph is a fundamental problem and has been extensively investigated. Most of the existing approaches focus on finding a community in a social network, and very few studies consider location-based social networks where users can check in locations. In this paper we propose the GeoSocial Community Search problem (GCS) which aims to find a social community and a cluster of spatial locations that are densely connected in a location-based social network simultaneously. The GCS can be useful for marketing and user/location recommendation. To the best of our knowledge, this is the first work to find a social community and a cluster of spatial loc..View full abstract
Awarded by MOE Tier-2 grant
Awarded by MOE Tier-1 grant
Gao Cong is partially supported by Singtel Cognitive and Artificial Intelligence Lab for Enterprises (SCALE@NTU), which is a collaboration between Singapore Telecommunications Limited (Singtel) and Nanyang Technological University (NTU) that is funded by the Singapore Government through the Industry Alignment Fund - Industry Collaboration Projects Grant, a MOE Tier-2 grant MOE2016-T2-1-137, and a NTU ACE grant. Arijit Khan is supported by MOE Tier-1 grant 2019-T1-002-059.