I/O efficient k-truss community search in massive graphs

• Published in: The VLDB journal Vol. 30; no. 5; pp. 713 - 738
• Main Authors: Jiang, Yuli, Huang, Xin, Cheng, Hong
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2021; Springer Nature B.V
• Subjects: Algorithms; Apexes; Computer Science; Database Management; Graph theory; Graphs; Queries; Query processing; Regular Paper; Search algorithms; Supports; Trusses; dynamic graphs; semi-external algorithms; community search; truss
• ISSN: 1066-8888; 0949-877X
• DOI: 10.1007/s00778-020-00649-y

Community detection that discovers all densely connected communities in a network has been studied a lot. In this paper, we study online community search for query-dependent communities, which is a different but practically useful task. Given a query vertex in a graph, the problem is to find meaningful communities that the vertex belongs to in an online manner. We propose a community model based on the k -truss concept, which brings nice structural and computational properties. We design a compact and elegant index structure which supports the efficient search of k -truss communities with a linear cost with respect to the community size. We also investigate the k -truss community search problem in a dynamic graph setting with frequent insertions and deletions of graph vertices and edges. In addition, to support k -truss community search over massive graphs which cannot entirely fit in main memory, we propose I/O-efficient algorithms for query processing under the semi-external model. Extensive experiments on massive real-world networks demonstrate the effectiveness of our k -truss community model, the efficiency, and the scalability of our in-memory and semi-external community search algorithms.