Cohesive Group Nearest Neighbor Queries on Road-Social Networks under Multi-Criteria

Abstract

The group nearest neighbor (GNN) search on a road network <inline-formula><tex-math notation="LaTeX">$G_r$</tex-math></inline-formula> , i.e., finding the spatial objects as activity assembly points with the smallest sum of distances to query users on <inline-formula><tex-math notation="LaTeX">$G_r$</tex-math></inline-formula> , has been extensively studied; however, previous works neglected the fact that social relationships among query users, which ensure the maximally favorable atmosphere in the activity, can play an important role in GNN queries. Meanwhile, the ratings of spatial objects can also be used as recommended guidelines. Many real-world applications, such as location-based social networking services, require such queries. In this paper, we study two new problems: (1) a GNN search on a road network that incorporates cohesive social relationships (CGNN) and (2) a CGNN query under multi-criteria (MCGNN). Specifically, both the query users of highest closeness and the corresponding top- <inline-formula><tex-math notation="LaTeX">$j$</tex-math></inline-formula> objects are retrieved. To address critical challenges on the effectiveness of results and the efficiency of computation over large road-social networks: (1) for CGNN, we propose a filtering-and-verification framework. During filtering, we prune substantial unpromising users and objects using social and geospatial constraints. During verification, we obtain the object candidates, among which the top <inline-formula><tex-math notation="LaTeX">$j$</tex-math></inline-formula> are selected, with respect to the qualified users; (2) for MCGNN, we propose threshold-based selection and expansion strategies, where different strict boundaries are proposed to ensure that correct top- <inline-formula><tex-math notation="LaTeX">$j$</tex-math></inline-formula> objects are found early. Moreover, we further optimize search strategies to improve query performance. Finally, experimental results on real social and road networks significantly demonstrate the efficiency and efficacy of our solutions.