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

Chengbin Peng; Zhihua Zhang; Ka-Chun Wong; Xiangliang Zhang; David E. Keyes

doi:10.3233/IDA-163156

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

Chengbin Peng, Zhihua Zhang, Ka-Chun Wong, Xiangliang Zhang, David E. Keyes

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4 Scopus citations

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

Original language	English (US)
Pages (from-to)	1463-1485
Number of pages	23
Journal	Intelligent Data Analysis
Volume	21
Issue number	6
DOIs	https://doi.org/10.3233/IDA-163156
State	Published - Nov 24 2017

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: Research reported in this publication was supported by King Abdullah University of Science and Technology (KAUST).

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10.3233/IDA-163156

https://repository.kaust.edu.sa/bitstream/10754/626776/1/A%20scalable%20community%20detection%20algorithm%20for%20large%20graphs%20using%20stochastic%20block%20models.pdf

Cite this

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title = "A scalable community detection algorithm for large graphs using stochastic block models",

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",

author = "Chengbin Peng and Zhihua Zhang and Ka-Chun Wong and Xiangliang Zhang and Keyes, {David E.}",

note = "KAUST Repository Item: Exported on 2020-10-01 Acknowledgements: Research reported in this publication was supported by King Abdullah University of Science and Technology (KAUST).",

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AU - Wong, Ka-Chun

AU - Zhang, Xiangliang

AU - Keyes, David E.

N1 - KAUST Repository Item: Exported on 2020-10-01 Acknowledgements: Research reported in this publication was supported by King Abdullah University of Science and Technology (KAUST).

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N2 - 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

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

Abstract

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