  Recently, the community search problem has attracted significant attention,
due to its wide spectrum of real-world applications such as event organization,
friend recommendation, advertisement in e-commence, and so on. Given a query
vertex, the community search problem finds dense subgraph that contains the
query vertex. In social networks, users have multiple check-in locations,
influence score, and profile information (keywords). Most previous studies that
solve the CS problem over social networks usually neglect such information in a
community. In this paper, we propose a novel problem, named community search
over spatial-social networks (TCS-SSN), which retrieves community with high
social influence, small traveling time, and covering certain keywords. In order
to tackle the TCS-SSN problem over the spatial-social networks, we design
effective pruning techniques to reduce the problem search space. We also
propose an effective indexing mechanism, namely social-spatial index, to
facilitate the community query, and develop an efficient query answering
algorithm via index traversal. We verify the efficiency and effectiveness of
our pruning techniques, indexing mechanism, and query processing algorithm
through extensive experiments on real-world and synthetic data sets under
various parameter settings.
