Paper Info
Title
Querying k-truss community in large and dynamic graphs
Abstract
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 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. In addition, we investigate the k-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.
Year
DOI
Venue
2014
10.1145/2588555.2610495
SIGMOD Conference
Keywords
Field
DocType
dynamic graph,community search,k-truss,graph algorithms,data mining
Data mining,Search algorithm,Online community,Vertex (geometry),Computer science,Level structure,Theoretical computer science,Null graph,Bidirectional search,Database,Feedback vertex set,Voltage graph
Conference
Citations 
PageRank 
References 
93
2.01
19
Authors
5
Name
Order
Citations
PageRank
Xin Huang137929.42
Hong Cheng23694148.72
Lu Qin3143095.44
Wentao Tian41464.23
Jeffrey Xu Yu57018464.96