Efficient Radius-Bounded Community Search in Geo-Social Networks

Driven by real-life applications in geo-social networks, we study the problem of computing radius-bounded
$k$
-cores (RB-
$k$
-cores) that aims to find communities satisfying both social and spatial constraints. In particular, the model
$k$
-core (i.e., the subgraph where each vertex has at least
$k$
neighbors) is used to ensure the social cohesiveness, and a radius-bounded circle is used to restrict the locations of users in an RB-
$k$
-core. We explore several algorithmic paradigms to compute RB-
$k$
-cores, including a triple-vertex-based paradigm, a binary-vertex-based paradigm, and a paradigm utilizing the concept of rotating circles. The rotating-circle-based paradigm is further enhanced by several pruning techniques to achieve better efficiency. In addition, to find representative RB-
$k$
-cores, we study the diversified radius-bounded
$k$
-core search problem, which finds
$t$
RB-
$k$
-cores to cover the most number of vertices. We first propose a baseline algorithm that identifies the distinctive RB-
$k$
-cores after finding all the RB-
$k$
-cores. Beyond this, we design algorithms that can efficiently maintain the top-
$t$
candidate RB-
$k$
-cores and also achieve a guaranteed approximation ratio. Experimental studies on both real and synthetic datasets demonstrate that our proposed techniques can efficiently compute (diversified) RB-
$k$
-cores. Moreover, our techniques can be used to compute the minimum-circle-bounded
$k$
-core and significantly outperform the existing techniques.