A scalable community detection algorithm for large graphs using stochastic block models
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A scalable community detection algorithm for large graphs using stochastic block models.pdf
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KAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionComputer Science Program
Applied Mathematics and Computational Science Program
Extreme Computing Research Center
Date
2017-11-24Permanent link to this record
http://hdl.handle.net/10754/626776Metadata
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Community detection in graphs is widely used in social and biological networks, and the stochastic block model is a powerful probabilistic tool for describing graphs with community structures. However, in the era ofCitation
Chengbin Peng, Zhihua Zhang, Ka-Chun Wong, Xiangliang Zhang, David E. Keyes. A scalable community detection algorithm for large graphs using stochastic block models. IDA. IOS Press; 2017;21: 1463–1485. doi:10.3233/IDA-163156Sponsors
Research reported in this publication was supported by King Abdullah University of Science and Technology (KAUST).Publisher
IOS PressJournal
Intelligent Data AnalysisAdditional Links
https://content.iospress.com/articles/intelligent-data-analysis/ida163156https://www.ijcai.org/Proceedings/15/Papers/296.pdf