Efficient Search of the Most Cohesive Co-located Community in Attributed Networks

Abstract

Attributed networks are used to model various networks, such as social networks, knowledge graphs, and protein-protein interactions. Such networks are associated with rich attributes such as spatial locations (e.g., check-ins from social network users and positions of proteins). The community search in 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 most cohesive co-located community ($\textsc {MC}^{3}$), which returns communities that satisfy the following two properties: (i) structural cohesiveness: members in the community are connected the most intensively; (ii) spatial co-location: members are close to each other. The problem can be used for social network user behavior analysis, recommendation, disease predication etc. We first propose an index structure called $\textsc {D}k\textsc {Q-tree}$ to integrate the spatial information and the local structure information together to accelerate the query processing. Then, based on this index structure we develop two efficient algorithms. The extensive experiments conducted on both real and synthetic datasets demonstrate the efficiency and effectiveness of the proposed methods.