Precomputing Hybrid Index Architecture for Flexible Community Search over Location-Based Social Networks

Community search is defined as finding query-based communities within simple graphs. One of the most crucial community models is minimum degree subgraph in which each vertex has at least k neighbours. Due to the rapid development of location-based devices; however, simple graphs are unable to handle Location-Based Social Networks LBSN personal information such as interests and spatial locations. Hence, this paper aims to construct a Precomputed Hybrid Index Architecture (PHIA) for the sake of enhancing simple graphs to store and retrieve information of LBSN users. This method consists of two stages; the first is precomputing, and the second is index construction. Numerical testing showed that our hybrid index approach is reasonable because of its flexibility to combine different dimensions by adapting the wide used community model k - core.