  With the urbanization and development of infrastructure, the community search
over road networks has become increasingly important in many real applications
such as urban/city planning, social study on local communities, and community
recommendations by real estate agencies. In this paper, we propose a novel
problem, namely top-k community similarity search (Top-kCS2) over road
networks, which efficiently and effectively obtains k spatial communities that
are the most similar to a given query community in road-network graphs. In
order to efficiently and effectively tackle the Top-kCS2 problem, in this
paper, we will design an effective similarity measure between spatial
communities, and propose a framework for retrieving Top-kCS2 query answers,
which integrates offline pre-processing and online computation phases.
Moreover, we also consider a variant, namely continuous top-k community
similarity search (CTop-kCS2), where the query community continuously moves
along a query line segment. We develop an efficient algorithm to split query
line segments into intervals, incrementally obtain similar candidate
communities for each interval and define actual CTop-kCS2 query answers.
Extensive experiments have been conducted on real and synthetic data sets to
confirm the efficiency and effectiveness of our proposed Top-kCS2 and CTop-kCS2
approaches under various parameter setting
