Publication | Closed Access
On socio-spatial group query for location-based social networks
118
Citations
10
References
2012
Year
Unknown Venue
Crowd ComputingComputational Social ScienceNetwork ScienceEfficient Algorithm SsgselectData ScienceData MiningEngineeringNew Index StructureSocial ComputingGeosocial NetworkSpatial NetworkComputer ScienceLocation-aware Social MediumCommunicationTight Social RelationCombinatorial OptimizationSocio-spatial Group QuerySocial Network Analysis
Challenges faced in organizing impromptu activities are the requirements of making timely invitations in accordance with the locations of candidate attendees and the social relationship among them. It is desirable to find a group of attendees close to a rally point and ensure that the selected attendees have a good social relationship to create a good atmosphere in the activity. Therefore, this paper proposes Socio-Spatial Group Query (SSGQ) to select a group of nearby attendees with tight social relation. Efficient processing of SSGQ is very challenging due to the tradeoff in the spatial and social domains. We show that the problem is NP-hard via a proof and design an efficient algorithm SSGSelect, which includes effective pruning techniques to reduce the running time for finding the optimal solution. We also propose a new index structure, Social R-Tree to further improve the efficiency. User study and experimental results demonstrate that SSGSelect significantly outperforms manual coordination in both solution quality and efficiency.
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