A scalable community detection algorithm for large graphs using stochastic block models

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Article
Authors
Peng, Chengbin cc
Zhang, Zhihua
Wong, Ka-Chun
Zhang, Xiangliang cc
Keyes, David E. cc
KAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Computer Science Program
Applied Mathematics and Computational Science Program
Extreme Computing Research Center
Date
2017-11-24

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Abstract
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 of
Citation
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-163156
Sponsors
Research reported in this publication was supported by King Abdullah University of Science and Technology (KAUST).
Publisher
IOS Press
Journal
Intelligent Data Analysis
ISSN
1088-467X
1571-4128
DOI
10.3233/IDA-163156
Handle URI
http://hdl.handle.net/10754/626776
Additional Links
https://content.iospress.com/articles/intelligent-data-analysis/ida163156
Collections
Articles; Applied Mathematics and Computational Science Program; Extreme Computing Research Center; Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division