On Attributed Community Search

Abstract

Communities, which are prevalent in attributed graphs (e.g., social networks and knowledge bases) can be used in emerging applications such as product advertisement and setting up of social events. Given a graph G and a vertex \(q \in G\), the community search (CS) query returns a subgraph of G that contains vertices related to q. In this article, we study CS over two common attributed graphs, where (1) vertices are associated with keywords; and (2) vertices are augmented with locations. For keyword-based attributed graphs, we investigate the keyword-based attributed community (or KAC) query, which returns a KAC for a query vertex. A KAC satisfies both structure cohesiveness (i.e., its vertices are tightly connected) and keyword cohesiveness (i.e., its vertices share common keywords). For spatial-based attributed graphs, we aim to find the spatial-aware community (or SAC), whose vertices are close structurally and spatially, for a query vertex in an online manner. To enable efficient KAC search and SAC search, we propose efficient query algorithms. We also perform experimental evaluation on large real datasets, and the results show that our methods achieve higher effectiveness than the state-of-the-art community retrieval algorithms. Moreover, our solutions are faster than baseline approaches. In addition, we develop the C-Explorer system to assist users in extracting, visualizing, and analyzing KACs.