Efficient Radius-Bounded Community Search in Geo-Social Networks
Kai Wang, Shuting Wang, Xin Cao, Lu Qin · IEEE Transactions on Knowledge and Data Engineering · 2020
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.