Efficient Radius-Bounded Community Search in Geo-Social Networks

Abstract

Driven by real-life applications in geo-social networks, we study the problem of computing radius-bounded <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -cores (RB- <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -cores) that aims to find communities satisfying both social and spatial constraints. In particular, the model <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -core (i.e., the subgraph where each vertex has at least <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> neighbors) is used to ensure the social cohesiveness, and a radius-bounded circle is used to restrict the locations of users in an RB- <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -core. We explore several algorithmic paradigms to compute RB- <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -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- <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -cores, we study the diversified radius-bounded <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -core search problem, which finds <inline-formula><tex-math notation="LaTeX">$t$</tex-math></inline-formula> RB- <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -cores to cover the most number of vertices. We first propose a baseline algorithm that identifies the distinctive RB- <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -cores after finding all the RB- <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -cores. Beyond this, we design algorithms that can efficiently maintain the top- <inline-formula><tex-math notation="LaTeX">$t$</tex-math></inline-formula> candidate RB- <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -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- <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -cores. Moreover, our techniques can be used to compute the minimum-circle-bounded <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -core and significantly outperform the existing techniques.