Maximum co-located community search in large scale social networks

Abstract

The problem of k-truss search has been well defined and investigated to find the highly correlated user groups in social networks. But there is no previous study to consider the constraint of users' spatial information in k-truss search, denoted as co-located community search in this paper. The co-located community can serve many real applications. To search the maximum co-located communities efficiently, we first develop an efficient exact algorithm with several pruning techniques. After that, we further develop an approximation algorithm with adjustable accuracy guarantees and explore more effective pruning rules, which can reduce the computational cost significantly. To accelerate the real-time efficiency, we also devise a novel quadtree based index to support the efficient retrieval of users in a region and optimise the search regions with regards to the given query region. Finally, we verify the performance of our proposed algorithms and index using five real datasets.