Best Co-Located Community Search in Attributed Networks

Various networks have rich attributes such as texts (e.g., tweets) and locations (e.g., check-ins). The community search in such attributed networks have been intensively studied recently due to its wide applications in recommendation, marketing, biology, etc. In this paper, we study the problem of searching the \underlineB est \underlineC o-located \underlineC ommunity (\BCC) in attributed networks, which returns a community that satisfies the following properties: i) structural cohesiveness: members in the community are densely connected, ii) spatial co-location: members are close to each other, and iii) quality optimality: the community has the best quality in terms of given attributes. The problem can be used in social network user behavior analysis, recommendation systems, disease predication, etc. We first propose an index structure called \DTree to integrate the spatial information, the local structure information, and the attribute information together to accelerate the query processing. Then, based on this index we develop an efficient algorithm. The experimental study conducted on both real and synthetic datasets demonstrate the efficiency and effectiveness of the proposed methods.