Top-$k$ Community Similarity Search Over Large-Scale Road Networks

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 article, we propose a novel problem, namely
top-$k$k community similarity search
(
$Top\text{-}kCS^{2}$
) 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
$Top\text{-}kCS^{2}$
problem, in this paper, we will design an effective similarity measure between spatial communities, and propose a framework for retrieving
$Top\text{-}kCS^{2}$
query answers, which integrates offline pre-processing and online computation phases. Moreover, we also consider a variant, namely
continuous top-$k$k community similarity search
(
$CTop\text{-}kCS^{2}$
), where the query community continuously moves along a query line segment. We develop an efficient algorithm to split query line segment into intervals, incrementally obtain similar candidate communities for each interval, and refine actual
$CTop\text{-}kCS^{2}$
query answers. Extensive experiments have been conducted on real and synthetic data sets to confirm the efficiency and effectiveness of our proposed
$Top\text{-}kCS^{2}$
and
$CTop\text{-}kCS^{2}$
approaches under various parameter settings.