Querying K-truss community in large and dynamic graphs

Publication Type:
Conference Proceeding
Citation:
Proceedings of the ACM SIGMOD International Conference on Management of Data, 2014, pp. 1311 - 1322
Issue Date:
2014-01-01
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Community detection which discovers densely connected structures in a network has been studied a lot. In this paper, we study online community search which is practically useful but less studied in the literature. 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 novel community model based on the κ-truss concept, which brings nice structural and computational properties. We design a compact and elegant index structure which supports the efficient search of κ-truss communities with a linear cost with respect to the community size. In addition, we investigate the κ truss community search problem in a dynamic graph setting with frequent insertions and deletions of graph vertices and edges. Extensive experiments on large real-world networks demonstrate the effectiveness and efficiency of our community model and search algorithms. © 2014 ACM.
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