The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
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.
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Cites
(1979) · Cited by 43,841
Finding and evaluating community structure in networks
Newman, M. E. J., & Girvan, M. (2004) · Physical Review E · doi:10.1103/physreve.69.026113 · Cited by 13,931
Uncovering the overlapping community structure of complex networks in nature and society
Palla, G., Derényi, I., Farkas, I., & Vicsek, T. (2005) · Nature · doi:10.1038/nature03607 · Cited by 5,373
Maps of random walks on complex networks reveal community structure
Rosvall, M., & Bergstrom, C. T. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0706851105 · Cited by 4,583
Algorithm 457: finding all cliques of an undirected graph
Bron, C., & Kerbosch, J. (1973) · Communications of the ACM · doi:10.1145/362342.362367 · Cited by 2,452
Link communities reveal multiscale complexity in networks
Ahn, Y.-Y., Bagrow, J. P., & Lehmann, S. (2010) · Nature · doi:10.1038/nature09182 · Cited by 1,949
Learning to Discover Social Circles in Ego Networks
Leskovec, J., & McAuley, J. (2012) · neural information processing systems · Cited by 1,707
Overlapping community detection in networks: The state-of-the-art and comparative study
Xie, J., Kelley, S., & Szymanski, B. K. (2013) · ACM Computing Surveys · doi:10.1145/2501654.2501657 · Cited by 930
Overlapping Community Detection in Networks: the State of the Art and Comparative Study
Xie, J., Kelley, S., & Szymanski, B. K. (2011) · arXiv (Cornell University) · doi:10.48550/arxiv.1110.5813 · Cited by 865
Structural diversity in social contagion
Ugander, J., Backstrom, L., Marlow, C., & Kleinberg, J. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1116502109 · Cited by 695
Cited by
A survey of community search over big graphs
Fang, Y., Huang, X., Qin, L., Zhang, Y., Zhang, W., Cheng, R., & Lin, X. (2020) · The VLDB Journal · doi:10.1007/s00778-019-00556-x · Cited by 339
Self-citationEffective community search for large attributed graphs
Fang, Y., Cheng, R., Luo, S., & Hu, J. (2016) · Proceedings of the VLDB Endowment · doi:10.14778/2994509.2994538 · Cited by 317
Metadata
- Authors
- Huang, X., Cheng, H., Qin, L., Tian, W., & Yu, J. X.
- Venue
- Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data
- Publisher
- ACM
- Published
- 2014-06-18
- Pages
- 1311-1322
- ISSN
- 0730-8078 (ISSN-L)
- OpenAlex
- W2125895010
- Cited by
- 488
- References
- 19
- FWCI
- 9.73
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Huang, X., Cheng, H., Qin, L., Tian, W., & Yu, J. X. (2014). Querying k-truss community in large and dynamic graphs. Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data. https://doi.org/10.1145/2588555.2610495
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