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A review of stochastic block models and extensions for graph clustering

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A review of stochastic block models and extensions for graph clustering. / Lee, Clement; Wilkinson, Darren J.
In: Applied Network Science, Vol. 4, No. 1, 122, 23.12.2019.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Lee, C & Wilkinson, DJ 2019, 'A review of stochastic block models and extensions for graph clustering', Applied Network Science, vol. 4, no. 1, 122. https://doi.org/10.1007/s41109-019-0232-2

APA

Lee, C., & Wilkinson, D. J. (2019). A review of stochastic block models and extensions for graph clustering. Applied Network Science, 4(1), Article 122. https://doi.org/10.1007/s41109-019-0232-2

Vancouver

Lee C, Wilkinson DJ. A review of stochastic block models and extensions for graph clustering. Applied Network Science. 2019 Dec 23;4(1):122. doi: 10.1007/s41109-019-0232-2

Author

Lee, Clement ; Wilkinson, Darren J. / A review of stochastic block models and extensions for graph clustering. In: Applied Network Science. 2019 ; Vol. 4, No. 1.

Bibtex

@article{c18f6bd8828a4b02a3f06316bfe0e830,
title = "A review of stochastic block models and extensions for graph clustering",
abstract = "There have been rapid developments in model-based clustering of graphs, also known as block modelling, over the last ten years or so. We review different approaches and extensions proposed for different aspects in this area, such as the type of the graph, the clustering approach, the inference approach, and whether the number of groups is selected or estimated. We also review models that combine block modelling with topic modelling and/or longitudinal modelling, regarding how these models deal with multiple types of data. How different approaches cope with various issues will be summarised and compared, to facilitate the demand of practitioners for a concise overview of the current status of these areas of literature.",
keywords = "Model-based clustering, Stochastic block models, Mixed membership models, Topic modelling, Longitudinal modelling, Statistical inference",
author = "Clement Lee and Wilkinson, {Darren J.}",
year = "2019",
month = dec,
day = "23",
doi = "10.1007/s41109-019-0232-2",
language = "English",
volume = "4",
journal = "Applied Network Science",
publisher = "SpringerOpen",
number = "1",

}

RIS

TY - JOUR

T1 - A review of stochastic block models and extensions for graph clustering

AU - Lee, Clement

AU - Wilkinson, Darren J.

PY - 2019/12/23

Y1 - 2019/12/23

N2 - There have been rapid developments in model-based clustering of graphs, also known as block modelling, over the last ten years or so. We review different approaches and extensions proposed for different aspects in this area, such as the type of the graph, the clustering approach, the inference approach, and whether the number of groups is selected or estimated. We also review models that combine block modelling with topic modelling and/or longitudinal modelling, regarding how these models deal with multiple types of data. How different approaches cope with various issues will be summarised and compared, to facilitate the demand of practitioners for a concise overview of the current status of these areas of literature.

AB - There have been rapid developments in model-based clustering of graphs, also known as block modelling, over the last ten years or so. We review different approaches and extensions proposed for different aspects in this area, such as the type of the graph, the clustering approach, the inference approach, and whether the number of groups is selected or estimated. We also review models that combine block modelling with topic modelling and/or longitudinal modelling, regarding how these models deal with multiple types of data. How different approaches cope with various issues will be summarised and compared, to facilitate the demand of practitioners for a concise overview of the current status of these areas of literature.

KW - Model-based clustering

KW - Stochastic block models

KW - Mixed membership models

KW - Topic modelling

KW - Longitudinal modelling

KW - Statistical inference

U2 - 10.1007/s41109-019-0232-2

DO - 10.1007/s41109-019-0232-2

M3 - Journal article

VL - 4

JO - Applied Network Science

JF - Applied Network Science

IS - 1

M1 - 122

ER -