Effective and efficient relational community detection and search in large dynamic heterogeneous information networks
Xun Jian, Yue Wang, Lei Chen
Abstract
Community search in heterogeneous information networks (HINs) has attracted much attention in graph analysis. Given a vertex, the goal is to find a densely-connected sub-graph that contains the vertex. In practice, the user may need to restrict the number of connections between vertices, but none of the existing methods can handle such queries. In this paper, we propose the relational constraint that allows the user to specify fine-grained connection requirements between vertices. Base on this, we define the relational community as well as the problems of detecting and searching relational communities, respectively. For the detection problem, we propose an efficient solution that has near-linear time complexity. For the searching problem, although it is shown to be NP-hard and even hard-to-approximate, we devise two efficient approximate solutions. We further design the round index to accelerate the searching algorithm and show that it can handle dynamic graphs by its nature. Extensive experiments on both synthetic and real-world graphs are conducted to evaluate both the effectiveness and efficiency of our proposed methods.